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The Tourist

When a man wakes up in the Australian outback with no memory, he must use the few clues he has to discover his identity before his past catches up with him. When a man wakes up in the Australian outback with no memory, he must use the few clues he has to discover his identity before his past catches up with him. When a man wakes up in the Australian outback with no memory, he must use the few clues he has to discover his identity before his past catches up with him.

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• Jack Williams
• Jamie Dornan
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• Greg Larsen
• 353 User reviews
• 42 Critic reviews
• 4 wins & 13 nominations total

Episodes 12

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• Trivia The reason season 2 is set in Ireland was because Jamie Dorman didn't want to spend several month away in Australia again. The decision was made to relocate so Jamie could be close to his family
• Connections Featured in Jeremy Vine: Episode #5.5 (2022)

User reviews 353

• Feb 24, 2024
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• March 3, 2022 (United States)
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Colin from Accounts Season 2 streaming now on BINGE!

All the best sci-fi shows streaming on stan in june 2024.

If you're looking for the best Sci-Fi Shows then you’ve come to the right place. In June 2024 Stan has around 32 on offer. Below are the top 20 latest and greatest Sci-Fi Shows streaming on the service

The top 20 best Sci-Fi Shows Streaming on Stan by rating.

Young Justice

The Handmaid's Tale

Orphan Black

Bravest Warriors

Harvey Birdman, Attorney at Law

3rd Rock from the Sun

My Little Pony: Friendship Is Magic

Project Blue Book

Red Dwarf: The Promised Land

The Man Who Fell to Earth

Beauty and the Beast

Made for Love

Transformers: Rescue Bots

The Midwich Cuckoos

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The top 5 Sci-Fi Shows Streaming on Stan in Australia.

Seasons: 13

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Seasons: 12

Rated: TV-14

Stan: new shows and films streaming in November 2023

Australian series Scrublands comes to streaming this month. Image: Stan.

Don’t know what to watch but have a Stan account? Here’s our guide to the shows and films coming to the platform in Australia this month.

Orphan Black Echoes

Starring Krysten Ritter and Keeley Hawes, this sci-fi thriller series is set in the same universe as  Orphan Black and explores the scientific manipulation of human existence. Set in 2052, it follows Lucy (Ritter) and a group of women as they weave their way into each other’s lives and embark on a thrilling journey, unravelling the mystery of their identity as they try to find their place in the world.

57th CMA Awards

The 57th Annual CMA Awards broadcast live from Nashville. Co-hosted by country music icon Luke Bryan and former footballer Peyton Manning, the CMA Awards is the longest running, annual music awards program on network television. This year, nominees reflect some of Country’s biggest stars including Lainey Wilson, Luke Combs, Morgan Wallen, Carrie Underwood and Ashley McBryde

Arctic Circle – Season 3

The Finnish-German crime drama returns for an icy third season.

The Lazarus Project – Season 2

Starring Paapa Essiedu, Anjli   Mohindra, Vinette Robinson and Rudi   Dharmalingam, this time-bending sci-fi thriller follows George (Essiedu) as he relives the same day over and over again. In Season 2, the Lazarus agents find themselves trapped in a unique loop. This time, George and the team must exchange their mind-bending loops for the thrills and adventures of true time travel.

ARIA Awards 2023

For the first time ever, Stan brings the ARIA Awards live to Australian audiences. The full show will also be available on demand on Stan following the official live stream.

Luke Arnold, Bella Heathcote and   Jay Ryan star in this crime thriller based on the bestselling novel by Chris Hammer.   Set in the isolated and struggling town of Riversend, the series begins with the town’s charismatic and dedicated young priest Byron Swift   (Ryan) as he calmly opens fire on his congregation, killing five parishioners. 

Sort Of – Season 3

Sort Of. Image: Stan.

Created by and starring Bilal Baig, the coming-of-age story returns for its final season. The series follows Sabi Mehboob, a gender fluid millennial who straddles various identities, from sexy bartender at an LGBTQ bar to the youngest child in a large Pakistani family, to the de facto parent of a downtown hipster family.

Jones Family Christmas (2023)

Heather Mitchell,   Ella Scott Lynch, Max McKenna, Neil Melville , and Nicholas Denton, this Stan Original Film   is billed as a warm-hearted multi-generational comedy told through the lens of an Australian family whose Christmas is impacted by bushfires.

Paul Dalgarno

Paul Dalgarno is author of the novels A Country of Eternal Light (2023) and Poly (2020); the memoir And You May Find Yourself (2015); and the creative non-fiction book Prudish Nation (2023). He was formerly Deputy Editor of The Conversation and joined ScreenHub as Managing Editor in 2022. X: @pauldalgarno. Insta: @dalgarnowrites

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20 Of The Best Shows On Stan To Stream In 2024

By Jessica Best

3rd Jan 2024

At Urban List, we know that the hunt for the next Stan binge never ends so with that in mind, here’s a sling of the best shows to watch on Stan right now. In the mix, you’ll find all the complex drama and true crime to get stuck into, plus a bunch of newbies worth a solid binge, needless to say you won't be lost for all the best content to watch on Stan.

Read on for the best shows on Stan.

The brand new season of  Dr. Death  is based on the Wondery podcast and follows 'Miracle Man' Paolo Macchiarini (Édgar Ramírez), a charming surgeon renowned for his innovative operations. In the end, most patients perish by his hand, suffocating on the decaying plastic windpipes he placed inside them. 

From the award-winning makers of  Offspring , Year Of  is a multi-generational spin-off of your favourite Aussie serie  Bump.  Unsurprisngly, this one will showcase another set of disparate inner-city teens at Jubilee High School as they navigate their senior years. 

From the producers of  Better Call Saul  and  Breaking Bad ,  Lucky Hank  is a comedic drama series based on Richard Russo’s novel Straight Man . The series follows Bob Odenkirk as Henry (Hank) Devereaux, Jr., a failed novelist turned unlikely chair of the English department of a mediocre college in a working-class town. Backstabbing colleagues, crusading students and a flirtatious protégée all aggravate the mounting midlife crisis Hank experiences when his celebrated father flies back in the picture. 

Directed and written by Rian Johnson ( Knives Out ), this 10-part mystery-of-the-week series stars Academy® Award winner Adrien Brody ( The Pianist, The Grand Budapest Hotel ), Emmy® Award winner Joseph Gordon-Levitt ( Don Jon ) and Emmy® nominee Natasha Lyonne ( Orange is the New Black, Russian Doll) . The show follows Charlie Cale (Lyonne), a woman with the epic ability to determine when someone is lying.

Starring Travis Fimmel ( Vikings ), this gripping six-part crime drama series is the mystery of a classic whodunit. In 1994, seventeen-year-old Isabel Baker (Talijah Blackman-Corowa) was murdered, shocking the small town of Ashford and devastating Isabel’s Australian South Sea Islander community. The case was never solved, the killer never found. But 25 years later, the opening of a time capsule unearths a secret that puts cold-case detective James Cormack (Fimmel) on the trail of the killer.

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Yellowstone

The fifth season of the acclaimed Emmy-nominated series sees John Dutton (Kevin Costner) take office as the Governor of Montana causing the Dutton family to fight a war from all sides. The good news is that season five kicks off with a two-hour premiere event with all the ranch drama. 

House Of Ho

Inspired by the film Crazy Rich Asians, House of Ho is an unscripted series featuring the wealthy, Houston-based, multigenerational American-Vietnamese Ho family. Season two introduces new cast members including single roommates Bella Ho and Kim Ho, and independent-minded Vanessa Kon and Tammy Nguyễn (Nate’s sisters), and their respective partners Carlton and Tran, who each have slightly niche perspectives on what it means to be part of the Ho family and its legacy.

Raised By Refugees

From Kiwi comedian Pax Assadi, Raised By Refugees is the heartfelt comedy inspired by his own formative tween-age years growing up in Auckland. Pax, played by Kenus Binu, is a typical 12-year-old from the early 2000s, whose parents came to NZ in the late 80s as refugees. While his parents want him to get good grades, all Pax wants to do is fit in. But a major international incident makes that very difficult. 

The Midwich Cuckoos

Okay, if you're a Handmaid's Tale fanatic through and through, you're going to want to dig your mitts into The Midwich Cuckoos. Adpated from John Wyndham's 1957 sci-fi novel of the same name, this series is based in the small English village of Midwich where, women of the right childbearing age, mysteriously fall pregnant all at the same time.

The Lazarus Project

Starring Paapa Essiedu (Gangs of London),  The Lazarus Project  is a time-bending psychological thriller that follows an all-but-average bloke George, who wakes up one morning to find himself reliving a day from his months-long past. When George is recruited for the secret organisation called the Lazarus Project, he's expected to make the ultimate sacrifice, one he's not willing to make.

Queer As Folk

Set in New Orleans, one of the most unique queer communities in North America, Queer As Folk is a vibrant reimagining of the ground breaking British series created by Russell T. Davies. After having a unique relationship with the original Queer As Folk growing up, creator, writer and executive producer Stephen Dunn has developed a fresh series, so fresh that the storyline and plot has been kept on the down-low. 

Everything I Know About Love

Inspired by her own wildly funny, occasionally heart-breaking memoir, Dolly Alderton's  Everything I Know About Love  is an  unflinching account of surviving your 20s. From the producers of  Bridget Jones' Diary  and  Love Actually , this fresh series is written and created by Dolly Alderton, and directed by China Moo-Young. Set in a 2012 London sharehouse, with flashbacks to suburban adolescence in the early noughties, the Stan show recounts bad dates, heartaches and humiliations. 

Starring Emmy Award Winner Jean Smart, Hacks explores a dark mentorship that forms between the legendary Las Vegas comedian Deborah Vance (Jean Smart) and the entitled, outcast 25-year-old Ava Daniels (Hannah Einbinder). In season two,  it's Vance and Daniels against the world, as the once-relevant Vegas comedy queen must take her stand-up act on the road to small clubs across the country. 

Angelyne centres on the media personality who rose to prominence in 1984 after the appearance of a series of iconic billboards around L.A. Based on The Hollywood Reporter's investigative article, the limited series tells the story of fame, identity, and influencer culture before the world of reality TV and social media. 

RuPaul’s Drag Race All Stars

Season seven of RuPaul's Drag Race All Stars is the Olympics of drag starring only winners from previous seasons. Together, they compete for a $200,000 cash prize and the title of 'Queen of All Queens'. For the first time in Drag Race history, all eight returning queens have won previous seasons of Drag Race or All Stars. The cast includes RuPaul’s Drag Race winners that stretch from 2011 through 2020, one international queen and three queens who’ve won seasons of All Stars. The All Star cast will consist of Yvie Oddly, The Vivienne, Jinkx Monsoon, Raja, Jaida Essence Hall, Monét X Change, Shea Couleé and Trinity The Tuck. 

Better Call Saul

Starring Bob Odenkirk, the Emmy-nominated acclaimed drama series  Better Call Saul  returns for its sixth and final season. The series will conclude the ever complicated journey of Jimmy McGill (Bob Odenkirk), into criminal lawyer Saul Goodman. In case you missed it, the final season of the Breaking Bad spinoff will unite Walter White and Jesse Pinkman which is major news. 

Starring Academy Award Winners Julia Roberts and Sean Penn, Gaslit offers a new take on the Watergate Scandal that focuses on some untold stories and its forgotten characters. Based on the first season of the critically acclaimed Slow Burn podcast, the series centres on Martha Mitchell (Julia Roberts), a big personality with an even bigger mouth. 

Billy The Kid

From the creator of Vikings , Micahel Hirst, Billy The Kid tells the story of William H. Bonney (Tom Blyth), from his humble Irish roots, to his early days as a cowboy and gunslinger in the American frontier, to his pivotal role in the Lincoln County War. He meets Jesse Evans (Daniel Webber), another famous outlaw who intrigues Billy with his wild and reckless character, forever inviting him over to the dark side of life. ​

The Girl From Plainville

Starring Elle Fanning,  The Girl From Plainville  is inspired by the true story of Michelle Carter’s unprecedented “texting suicide” case. Based on the Esquire article of the same name by Jesse Barron, this show looks at Carter’s relationship with Conrad Roy III and the events that led to his death and, later, her conviction of involuntary manslaughter. 

Executive produced by Paul Feig (who you know from the likes of Bridesmaids, Love Life, Zoey's Extraordinary Playlist ), Minx centres around Joyce (Ophelia Lovibond), a young feminist who joins forces with a low-rent publisher (Jake Johnson) to create the first erotic magazine for women. In order to get her career on track, they have to navigate their way through the LA porn scene which, during the 1970s, was an absolute time. 

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Lock Your Doors Because Stan’s New Show ‘From’ Will Get In Your Head

From is a sci-fi/horror/thriller series starring Harold Perrineau ( Lost ), Catalina Sandino Moreno ( Maria Full of Grace ) and Eion Bailey ( Band of Brothers ). It unravels the mystery of a nightmarish town in middle America that traps all those who enter.

That last part was taken from Stan’s synopsis, and I truly wish I didn’t know that heading into watching episode one of From . Just a note that we won’t be spoiling the series, except for episode one. But don’t worry, there’s nine more to watch and we reckon you should definitely watch them all.

The first episode of From opens with an old-school American diner. It wasn’t scary by any means, but the whole scene was peppered with the looming fear that something bad was going to happen. This is either set back in the 70s or the townsfolk are just stuck in some sort of time-loop.

We’re introduced to a few of the characters, no one looks happy, and the cold open itself leaves a lot of unanswered questions. That’s where From hooks you. The opening credits are eerie, as We Gotta Get Out Of This Place by the Animals plays in the background. Subtle.

Then, we meet the Matthews family. A family of four travelling in an RV. They come across a fallen tree on the road, the catalyst for them entering the town you are yelling at the screen for them to avoid.

I won’t ruin too much more, but there’s murder, something supernatural, suspense, a town that somehow traps all who enter, brilliant cinematography and great acting.

I’ve left the trailer for last because I wish I didn’t know what was coming before I watched it, so if you’re keen on this, give the trailer a miss.

Stan’s From is gruesome, gripping and (looks at dictionary for another word starting with ‘G’)…good? It’s a little slow moving, but if they’re stuck in some sort of a time loop, it makes sense. The episode ends when it gets dark. We know we’re not safe after dark. Lock your doors, “They’re coming” .

You can catch From on Stan, with all 10 episodes dropping today, May 27.

‘South Park’: Stan, Kyle and Cartman Travel Back in Time to Prevent the Pandemic in Second Paramount+ Special (Video)

“South Park: Post Covid: The Return of Covid” premieres on Dec. 16

Paramount+ has set the premiere date for its second “South Park” special, which will wrap up the events from its first special, titled “Post Covid,” which debuted on Thanksgiving.

In the brief trailer above, an adult Stan, Kyle and Cartman travel back in time to try and prevent not only the death of their friend Kenny, but the COVID pandemic itself.

The special, “South Park: Post Covid: The Return of Covid,” will premiere on Dec. 16.

Here is the logline for “The Return of Covid”:

If Stan, Kyle and Cartman could just work together, they could go back in time to make sure Covid never happened and save Kenny’s life. In ‘South Park: Post Covid: The Return of Covid,’ traveling back to the past seems to be the easy answer until they meet Victor Chaos.

“Post Covid” took place well into the future, featuring grown up versions of Stan, Kyle, Cartman and Kenny in a world that was still living in the pandemic. Cartman was a reformed orthodox Jewish rabbi and Kenny a world-renowned scientist whose death sets in motion the events of the special. After it’s revealed that Kenny died from a new variant of Covid, the town of South Park goes back into lockdown, though a (very) old Randy Marsh and his now defunct Tegridy Weed business may hold the clue to stopping the pandemic.

“South Park” was renewed through Season 30 at Comedy Central, and 14 original movies based on the show have been ordered at Paramount+ as part of a six-year megdeal signed by co-creators Trey Parker and Matt Stone. New episodes of the show will still air first on Comedy Central.

Parker and Stone’s new pact with ViacomCBS’ MTV Entertainment Studios extends their partnership through 2027 and is worth $900 million .

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The 50+ Best Time Travel TV Shows, Ranked, Ranked

Are you ready to unravel the threads of time? This remarkable selection of the best time travel shows is filled with gripping narratives, memorable characters, and intriguing explorations of humanity's most complex enigma. These phenomenal series have not only captivated audiences but have also left an indelible mark on television history. Prepare to be taken on a thrilling ride through past, present, and future as these amazing tales challenge the limits of human imagination. 

The best time travel shows mesmerize viewers with their unique, thought-provoking explorations of temporal themes. With their gripping, groundbreaking, and original stories, these series immerse viewers in worlds where the rules of time are bent and broken. These exceptional shows have impacted the television industry at large, earning both pop culture prominence and critical acclaim for their unique takes on time-hopping adventures. 

Each offering in the best time travel TV shows selection offers a unique insight into the fascinating realm of temporal navigation. Embark on a mind-bending journey through different eras with Dark , where intricate storytelling keeps viewers on the edge of their seats. Delve into the heart-pounding mission to save humanity in 12 Monkeys , a show that blends compelling character arcs with stunning visuals and complex narrative. Experience the race against time in Timeless , a thrilling adventure filled with historical intrigue, unexpected twists, and powerful emotions. These remarkable shows are just a few of the outstanding lineup waiting to transport you through the ages. 

Time travel shows are popular and have had a profound impact on television culture because of the engaging narratives, dynamic characters, and thought-provoking themes. By providing riveting stories, unforgettable characters, and thought-provoking insights into the nature of time, these phenomenal series have solidified their places in the pantheon of the greatest time travel TV shows. So, sit back and prepare to lose track of time as audacious heroes take you on unforgettable voyages through the ages. 

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  For those who appreciate intricate storytelling, Dark is the perfect fit as it masterfully explores themes of time travel, human relationships, and existentialism. This German-language Netflix original gained immense popularity globally for its complexity and mind-bending narrative structure. Don't miss out on this modern sci-fi masterpiece that has been compared to Stranger Things  with a darker twist.

Is Dark Worth Your Time?

• # 165 of 302 on The Best TV Shows Of The Past 20 Years
• # 144 of 282 on The 260+ Best SciFi Shows Of All Time
• # 2 of 25 on The Best New Conspiracy TV Shows of the Last Few Years

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  Inspired by Terry Gilliam's cult classic film, Syfy's 12 Monkeys raises the stakes in science fiction television by expertly weaving together elements of time travel, suspense, and character-driven drama. The show boasts an incredibly talented cast led by Aaron Stanford and Amanda Schull, whose performances earned rave reviews from critics. Be prepared to be hooked on this intense yet thoughtfully crafted series that redefines the boundaries of traditional sci-fi storytelling.

Is 12 Monkeys Worth Your Time?

• # 92 of 282 on The 260+ Best SciFi Shows Of All Time
• # 9 of 20 on The Most Underrated Sci-Fi TV Shows Of The 2010s
• # 29 of 50 on TV Shows To Watch When You’re Raging Against The Machine

Quantum Leap

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  Quantum Leap is a beloved sci-fi classic well known for its unique premise and episodic adventures filled with heart and humor. Scott Bakula stars as Dr. Sam Beckett, who leaps through time to inhabit various lives while solving problems and altering history for the better. An essential watch for any fan of vintage television or science fiction, don't miss out on this entertaining journey through time.

Is Quantum Leap Worth Your Time?

• Dig Deeper... All Episodes Of 'Quantum Leap' (1989), Ranked By Fans
• # 156 of 343 on The Best TV Theme Songs of All Time
• # 22 of 281 on The 260+ Best SciFi Shows Of All Time

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  Drama meets adventure in Timeless as viewers follow a team of unlikely heroes tasked with chasing a dangerous fugitive through time. Tackling important historical events and figures, the show offers an enjoyable, educational, and thoughtfully presented fictional journey into America's past. Watch Timeless for its engaging pace, well-developed characters, and clever mix of history and science fiction.

Is Timeless Worth Your Time?

• # 70 of 282 on The 260+ Best SciFi Shows Of All Time
• # 73 of 355 on TV Shows Canceled Before Their Time
• # 245 of 657 on The 500+ Best Drama Shows

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  Based on Diana Gabaldon's best-selling novels, Outlander captivates viewers with its lush historical backdrop combined with elements of fantasy and romance. Set primarily in 18th century Scotland, the show seamlessly melds genres to create a visually stunning and emotionally poignant experience for fans of both history and fantasy alike. Tune in for the passionate love story between Claire Randall (Caitriona Balfe) and Jamie Fraser (Sam Heughan), but stay for the epic tale that unfolds across centuries.

Is Outlander Worth Your Time?

• Dig Deeper... 20 Fascinating Facts From Behind The Scenes Of 'Outlander'
• # 15 of 112 on The Best Drama TV Shows Airing Now
• # 16 of 145 on The Best Of Premium Cable TV Series

Paper Girls

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  Based on the acclaimed graphic novel series by Brian K. Vaughan and Cliff Chiang, Amazon Prime's Paper Girls follows a group of young girls in the 1980s who become entangled in a time-traveling war that spans across multiple eras. Fans can look forward to a thrilling adventure infused with nostalgic charm, heartfelt friendships, and thought-provoking themes about growing up amid extraordinary circumstances.

Is Paper Girls Worth Your Time?

• # 169 of 687 on The 600+ Best TV Shows Of The Last 5 Years
• # 934 of 1,056 on The 250+ Best Streaming Shows Of The Last Few Years
• # 116 of 308 on The Best Shows Canceled After a Single Season

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  A refreshing take on the time travel genre, Travelers introduces viewers to a compelling story about operatives sent back in time to prevent humanity's dystopian future. With standout performances from Eric McCormack and Mackenzie Porter, this Canadian-produced series delivers thrilling suspense while exploring themes of sacrifice and responsibility. Stream Travelers for an intelligent sci-fi experience that will leave you questioning your own reality.

Is Travelers Worth Your Time?

• # 57 of 281 on The 260+ Best SciFi Shows Of All Time
• # 299 of 657 on The 500+ Best Drama Shows
• # 4 of 20 on The Most Underrated Sci-Fi TV Shows Of The 2010s

The Twilight Zone

Watch This Show If You Love : Pineapple Express, Half Baked, Harold & Kumar Go to White Castle, How High, Friday Why Should I Stream?  With its timeless blend of horror, fantasy, and science fiction storytelling, The Twilight Zone remains one of television's most influential series ever created. From the mind of Rod Serling, this anthology program invites viewers to explore strange new worlds filled with eerie twists and social commentary still relevant today. Step into The Twilight Zone for a classic dose of unpredictable entertainment that defies convention.

Is The Twilight Zone Worth Your Time?

• Dig Deeper... The Best Twilight Zone Episodes of All Time
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• Entertainment
• Time Travel
• Watchworthy

As they say in well-written scripts, "You mean... like time travel?" + also a few bizarre stories about real people who have claimed, despite every law of physics, they have traveled through time.

Applied Time Series Analysis for Fisheries and Environmental Sciences

Chapter 13 stan for bayesian time series analysis.

For this lab, we will use Stan for fitting models. These examples are primarily drawn from the Stan manual and previous code from this class.

A script with all the R code in the chapter can be downloaded here . The Rmd for this chapter can be downloaded here

Data and packages

You will need the atsar and bayesdfa packages we have written for fitting state-space time series models with Stan. Install using the devtools package.

In addition, you will need the rstan , datasets , parallel and loo packages. After installing, if needed, load the packages:

Once you have Stan and rstan installed, optimize Stan on your machine:

For this lab, we will use a data set on air quality in New York from the datasets package. Load the data and create a couple new variables for future use.

Time-Series Models

Times series data come arranged in temporal order. This chapter presents two kinds of time series models, regression-like models such as autoregressive and moving average models, and hidden Markov models.

The Gaussian processes chapter presents Gaussian processes, which may also be used for time-series (and spatial) data.

Autoregressive models

A first-order autoregressive model (AR(1)) with normal noise takes each point \(y_n\) in a sequence \(y\) to be generated according to \[ y_n \sim \textsf{normal}(\alpha + \beta y_{n-1}, \sigma). \]

That is, the expected value of \(y_n\) is \(\alpha + \beta y_{n-1}\) , with noise scaled as \(\sigma\) .

AR(1) models

With improper flat priors on the regression coefficients \(\alpha\) and \(\beta\) and on the positively-constrained noise scale ( \(\sigma\) ), the Stan program for the AR(1) model is as follows. 1

The first observed data point, y[1] , is not modeled here because there is nothing to condition on; instead, it acts to condition y[2] . This model also uses an improper prior for sigma , but there is no obstacle to adding an informative prior if information is available on the scale of the changes in y over time, or a weakly informative prior to help guide inference if rough knowledge of the scale of y is available.

Slicing for efficiency

Although perhaps a bit more difficult to read, a much more efficient way to write the above model is by slicing the vectors, with the model above being replaced with the one-liner

The left-hand side slicing operation pulls out the last \(N-1\) elements and the right-hand side version pulls out the first \(N-1\) .

Extensions to the AR(1) model

Proper priors of a range of different families may be added for the regression coefficients and noise scale. The normal noise model can be changed to a Student- \(t\) distribution or any other distribution with unbounded support. The model could also be made hierarchical if multiple series of observations are available.

To enforce the estimation of a stationary AR(1) process, the slope coefficient beta may be constrained with bounds as follows.

In practice, such a constraint is not recommended. If the data are not well fit by a stationary model it is best to know this. Stationary parameter estimates can be encouraged with a prior favoring values of beta near zero.

AR(2) models

Extending the order of the model is also straightforward. For example, an AR(2) model could be coded with the second-order coefficient gamma and the following model statement.

AR( \(K\) ) models

A general model where the order is itself given as data can be coded by putting the coefficients in an array and computing the linear predictor in a loop.

ARCH(1) models

Econometric and financial time-series models usually assume heteroscedasticity: they allow the scale of the noise terms defining the series to vary over time. The simplest such model is the autoregressive conditional heteroscedasticity (ARCH) model ( Engle 1982 ) . Unlike the autoregressive model AR(1), which modeled the mean of the series as varying over time but left the noise term fixed, the ARCH(1) model takes the scale of the noise terms to vary over time but leaves the mean term fixed. Models could be defined where both the mean and scale vary over time; the econometrics literature presents a wide range of time-series modeling choices.

The ARCH(1) model is typically presented as the following sequence of equations, where \(r_t\) is the observed return at time point \(t\) and \(\mu\) , \(\alpha_0\) , and \(\alpha_1\) are unknown regression coefficient parameters.

\[\begin{align*} r_t &= \mu + a_t \\ a_t &= \sigma_t \epsilon_t \\ \epsilon_t &\sim \textsf{normal}(0,1) \\ \sigma^2_t &= \alpha_0 + \alpha_1 a_{t-1}^2 \end{align*}\]

In order to ensure the noise terms \(\sigma^2_t\) are positive, the scale coefficients are constrained to be positive, \(\alpha_0, \alpha_1 > 0\) . To ensure stationarity of the time series, the slope is constrained to be less than one, i.e., \(\alpha_1 < 1\) . 2

The ARCH(1) model may be coded directly in Stan as follows.

The loop in the model is defined so that the return at time \(t=1\) is not modeled; the model in the next section shows how to model the return at \(t=1\) . The model can be vectorized to be more efficient; the model in the next section provides an example.

Modeling temporal heteroscedasticity

A set of variables is homoscedastic if their variances are all the same; the variables are heteroscedastic if they do not all have the same variance. Heteroscedastic time-series models allow the noise term to vary over time.

GARCH(1,1) models

The basic generalized autoregressive conditional heteroscedasticity (GARCH) model, GARCH(1,1), extends the ARCH(1) model by including the squared previous difference in return from the mean at time \(t-1\) as a predictor of volatility at time \(t\) , defining \[ \sigma^2_t = \alpha_0 + \alpha_1 a^2_{t-1} + \beta_1 \sigma^2_{t-1}. \]

To ensure the scale term is positive and the resulting time series stationary, the coefficients must all satisfy \(\alpha_0, \alpha_1, \beta_1 > 0\) and the slopes \(\alpha_1 + \beta_1 < 1\) .

To get the recursive definition of the volatility regression off the ground, the data declaration includes a non-negative value sigma1 for the scale of the noise at \(t = 1\) .

The constraints are coded directly on the parameter declarations. This declaration is order-specific in that the constraint on beta1 depends on the value of alpha1 .

A transformed parameter array of non-negative values sigma is used to store the scale values at each time point. The definition of these values in the transformed parameters block is where the regression is now defined. There is an intercept alpha0 , a slope alpha1 for the squared difference in return from the mean at the previous time, and a slope beta1 for the previous noise scale squared. Finally, the whole regression is inside the sqrt function because Stan requires scale (deviation) parameters (not variance parameters) for the normal distribution.

With the regression in the transformed parameters block, the model reduces a single vectorized sampling statement. Because r and sigma are of length T , all of the data are modeled directly.

Moving average models

A moving average model uses previous errors as predictors for future outcomes. For a moving average model of order \(Q\) , \(\mbox{MA}(Q)\) , there is an overall mean parameter \(\mu\) and regression coefficients \(\theta_q\) for previous error terms. With \(\epsilon_t\) being the noise at time \(t\) , the model for outcome \(y_t\) is defined by \[ y_t = \mu + \theta_1 \epsilon_{t-1} + \dotsb + \theta_Q \epsilon_{t-Q} + \epsilon_t, \] with the noise term \(\epsilon_t\) for outcome \(y_t\) modeled as normal, \[ \epsilon_t \sim \textsf{normal}(0,\sigma). \] In a proper Bayesian model, the parameters \(\mu\) , \(\theta\) , and \(\sigma\) must all be given priors.

MA(2) example

An \(\mbox{MA}(2)\) model can be coded in Stan as follows.

The error terms \(\epsilon_t\) are defined as transformed parameters in terms of the observations and parameters. The definition of the sampling statement (defining the likelihood) follows the definition, which can only be applied to \(y_n\) for \(n > Q\) . In this example, the parameters are all given Cauchy (half-Cauchy for \(\sigma\) ) priors, although other priors can be used just as easily.

This model could be improved in terms of speed by vectorizing the sampling statement in the model block. Vectorizing the calculation of the \(\epsilon_t\) could also be sped up by using a dot product instead of a loop.

Vectorized MA(Q) model

A general \(\mbox{MA}(Q)\) model with a vectorized sampling probability may be defined as follows.

Here all of the data are modeled, with missing terms just dropped from the regressions as in the calculation of the error terms. Both models converge quickly and mix well at convergence, with the vectorized model being faster (per iteration, not to converge—they compute the same model).

Autoregressive moving average models

Autoregressive moving-average models (ARMA), combine the predictors of the autoregressive model and the moving average model. An ARMA(1,1) model, with a single state of history, can be encoded in Stan as follows.

The data are declared in the same way as the other time-series regressions and the parameters are documented in the code.

In the model block, the local vector nu stores the predictions and err the errors. These are computed similarly to the errors in the moving average models described in the previous section.

The priors are weakly informative for stationary processes. The likelihood only involves the error term, which is efficiently vectorized here.

Often in models such as these, it is desirable to inspect the calculated error terms. This could easily be accomplished in Stan by declaring err as a transformed parameter, then defining it the same way as in the model above. The vector nu could still be a local variable, only now it will be in the transformed parameter block.

Wayne Folta suggested encoding the model without local vector variables as follows.

This approach to ARMA models illustrates how local variables, such as err in this case, can be reused in Stan. Folta’s approach could be extended to higher order moving-average models by storing more than one error term as a local variable and reassigning them in the loop.

Both encodings are fast. The original encoding has the advantage of vectorizing the normal distribution, but it uses a bit more memory. A halfway point would be to vectorize just err .

Identifiability and stationarity

MA and ARMA models are not identifiable if the roots of the characteristic polynomial for the MA part lie inside the unit circle, so it’s necessary to add the following constraint 3

When the model is run without the constraint, using synthetic data generated from the model, the simulation can sometimes find modes for ( theta , phi ) outside the \([-1,1]\) interval, which creates a multiple mode problem in the posterior and also causes the NUTS tree depth to get large (often above 10). Adding the constraint both improves the accuracy of the posterior and dramatically reduces the tree depth, which speeds up the simulation considerably (typically by much more than an order of magnitude).

Further, unless one thinks that the process is really non-stationary, it’s worth adding the following constraint to ensure stationarity.

Stochastic volatility models

Stochastic volatility models treat the volatility (i.e., variance) of a return on an asset, such as an option to buy a security, as following a latent stochastic process in discrete time ( Kim, Shephard, and Chib 1998 ) . The data consist of mean corrected (i.e., centered) returns \(y_t\) on an underlying asset at \(T\) equally spaced time points. Kim et al. formulate a typical stochastic volatility model using the following regression-like equations, with a latent parameter \(h_t\) for the log volatility, along with parameters \(\mu\) for the mean log volatility, and \(\phi\) for the persistence of the volatility term. The variable \(\epsilon_t\) represents the white-noise shock (i.e., multiplicative error) on the asset return at time \(t\) , whereas \(\delta_t\) represents the shock on volatility at time \(t\) . \[\begin{align*} y_t &= \epsilon_t \exp(h_t / 2) \\ h_{t+1} &= \mu + \phi (h_t - \mu) + \delta_t \sigma \\ h_1 &\sim \textsf{normal}\left( \mu, \frac{\sigma}{\sqrt{1 - \phi^2}} \right) \\ \epsilon_t &\sim \textsf{normal}(0,1) \\ \delta_t &\sim \textsf{normal}(0,1) \end{align*}\]

Rearranging the first line, \(\epsilon_t = y_t \exp(-h_t / 2)\) , allowing the sampling distribution for \(y_t\) to be written as \[ y_t \sim \textsf{normal}(0,\exp(h_t/2)). \] The recurrence equation for \(h_{t+1}\) may be combined with the scaling and sampling of \(\delta_t\) to yield the sampling distribution \[ h_t \sim \mathsf{normal}(\mu + \phi(h_{t-1} - \mu), \sigma). \] This formulation can be directly encoded, as shown in the following Stan model.

Compared to the Kim et al. formulation, the Stan model adds priors for the parameters \(\phi\) , \(\sigma\) , and \(\mu\) . The shock terms \(\epsilon_t\) and \(\delta_t\) do not appear explicitly in the model, although they could be calculated efficiently in a generated quantities block.

The posterior of a stochastic volatility model such as this one typically has high posterior variance. For example, simulating 500 data points from the above model with \(\mu = -1.02\) , \(\phi = 0.95\) , and \(\sigma = 0.25\) leads to 95% posterior intervals for \(\mu\) of \((-1.23, -0.54)\) , for \(\phi\) of \((0.82, 0.98)\) , and for \(\sigma\) of \((0.16, 0.38)\) .

The samples using NUTS show a high degree of autocorrelation among the samples, both for this model and the stochastic volatility model evaluated in ( Hoffman and Gelman 2014 ) . Using a non-diagonal mass matrix provides faster convergence and more effective samples than a diagonal mass matrix, but will not scale to large values of \(T\) .

It is relatively straightforward to speed up the effective samples per second generated by this model by one or more orders of magnitude. First, the sampling statements for return \(y\) is easily vectorized to

This speeds up the iterations, but does not change the effective sample size because the underlying parameterization and log probability function have not changed. Mixing is improved by reparameterizing in terms of a standardized volatility, then rescaling. This requires a standardized parameter h_std to be declared instead of h .

The original value of h is then defined in a transformed parameter block.

The first assignment rescales h_std to have a \(\textsf{normal}(0,\sigma)\) distribution and temporarily assigns it to h . The second assignment rescales h[1] so that its prior differs from that of h[2] through h[T] . The next assignment supplies a mu offset, so that h[2] through h[T] are now distributed \(\textsf{normal}(\mu,\sigma)\) ; note that this shift must be done after the rescaling of h[1] . The final loop adds in the moving average so that h[2] through h[T] are appropriately modeled relative to phi and mu .

As a final improvement, the sampling statement for h[1] and loop for sampling h[2] to h[T] are replaced with a single vectorized standard normal sampling statement.

Although the original model can take hundreds and sometimes thousands of iterations to converge, the reparameterized model reliably converges in tens of iterations. Mixing is also dramatically improved, which results in higher effective sample sizes per iteration. Finally, each iteration runs in roughly a quarter of the time of the original iterations.

Hidden Markov models

A hidden Markov model (HMM) generates a sequence of \(T\) output variables \(y_t\) conditioned on a parallel sequence of latent categorical state variables \(z_t \in \{1,\ldots, K\}\) . These “hidden” state variables are assumed to form a Markov chain so that \(z_t\) is conditionally independent of other variables given \(z_{t-1}\) . This Markov chain is parameterized by a transition matrix \(\theta\) where \(\theta_k\) is a \(K\) -simplex for \(k \in \{ 1, \dotsc, K \}\) . The probability of transitioning to state \(z_t\) from state \(z_{t-1}\) is \[ z_t \sim \textsf{categorical}(\theta_{z[t-1]}). \] The output \(y_t\) at time \(t\) is generated conditionally independently based on the latent state \(z_t\) .

This section describes HMMs with a simple categorical model for outputs \(y_t \in \{ 1, \dotsc, V \}\) . The categorical distribution for latent state \(k\) is parameterized by a \(V\) -simplex \(\phi_k\) . The observed output \(y_t\) at time \(t\) is generated based on the hidden state indicator \(z_t\) at time \(t\) , \[ y_t \sim \textsf{categorical}(\phi_{z[t]}). \] In short, HMMs form a discrete mixture model where the mixture component indicators form a latent Markov chain.

Supervised parameter estimation

In the situation where the hidden states are known, the following naive model can be used to fit the parameters \(\theta\) and \(\phi\) .

Explicit Dirichlet priors have been provided for \(\theta_k\) and \(\phi_k\) ; dropping these two statements would implicitly take the prior to be uniform over all valid simplexes.

Start-state and end-state probabilities

Although workable, the above description of HMMs is incomplete because the start state \(z_1\) is not modeled (the index runs from 2 to \(T\) ). If the data are conceived as a subsequence of a long-running process, the probability of \(z_1\) should be set to the stationary state probabilities in the Markov chain. In this case, there is no distinct end to the data, so there is no need to model the probability that the sequence ends at \(z_T\) .

An alternative conception of HMMs is as models of finite-length sequences. For example, human language sentences have distinct starting distributions (usually a capital letter) and ending distributions (usually some kind of punctuation). The simplest way to model the sequence boundaries is to add a new latent state \(K+1\) , generate the first state from a categorical distribution with parameter vector \(\theta_{K+1}\) , and restrict the transitions so that a transition to state \(K+1\) is forced to occur at the end of the sentence and is prohibited elsewhere.

Calculating sufficient statistics

The naive HMM estimation model presented above can be sped up dramatically by replacing the loops over categorical distributions with a single multinomial distribution.

The data are declared as before. The transformed data block computes the sufficient statistics for estimating the transition and emission matrices.

The likelihood component of the model based on looping over the input is replaced with multinomials as follows.

In a continuous HMM with normal emission probabilities could be sped up in the same way by computing sufficient statistics.

Analytic posterior

With the Dirichlet-multinomial HMM, the posterior can be computed analytically because the Dirichlet is the conjugate prior to the multinomial. The following example illustrates how a Stan model can define the posterior analytically. This is possible in the Stan language because the model only needs to define the conditional probability of the parameters given the data up to a proportion, which can be done by defining the (unnormalized) joint probability or the (unnormalized) conditional posterior, or anything in between.

The model has the same data and parameters as the previous models, but now computes the posterior Dirichlet parameters in the transformed data block.

The posterior can now be written analytically as follows.

Semisupervised estimation

HMMs can be estimated in a fully unsupervised fashion without any data for which latent states are known. The resulting posteriors are typically extremely multimodal. An intermediate solution is to use semisupervised estimation, which is based on a combination of supervised and unsupervised data. Implementing this estimation strategy in Stan requires calculating the probability of an output sequence with an unknown state sequence. This is a marginalization problem, and for HMMs, it is computed with the so-called forward algorithm.

In Stan, the forward algorithm is coded as follows. First, two additional data variable are declared for the unsupervised data.

The model for the supervised data does not change; the unsupervised data are handled with the following Stan implementation of the forward algorithm.

The forward values gamma[t, k] are defined to be the log marginal probability of the inputs u[1],...,u[t] up to time t and the latent state being equal to k at time t ; the previous latent states are marginalized out. The first row of gamma is initialized by setting gamma[1, k] equal to the log probability of latent state k generating the first output u[1] ; as before, the probability of the first latent state is not itself modeled. For each subsequent time t and output j , the value acc[j] is set to the probability of the latent state at time t-1 being j , plus the log transition probability from state j at time t-1 to state k at time t , plus the log probability of the output u[t] being generated by state k . The log_sum_exp operation just multiplies the probabilities for each prior state j on the log scale in an arithmetically stable way.

The brackets provide the scope for the local variables acc and gamma ; these could have been declared earlier, but it is clearer to keep their declaration near their use.

Predictive inference

Given the transition and emission parameters, \(\theta_{k, k'}\) and \(\phi_{k,v}\) and an observation sequence \(u_1, \dotsc, u_T \in \{ 1, \dotsc, V \}\) , the Viterbi (dynamic programming) algorithm computes the state sequence which is most likely to have generated the observed output \(u\) .

The Viterbi algorithm can be coded in Stan in the generated quantities block as follows. The predictions here is the most likely state sequence y_star[1], ..., y_star[T_unsup] underlying the array of observations u[1], ..., u[T_unsup] . Because this sequence is determined from the transition probabilities theta and emission probabilities phi , it may be different from sample to sample in the posterior.

The bracketed block is used to make the three variables back_ptr , best_logp , and best_total_logp local so they will not be output. The variable y_star will hold the label sequence with the highest probability given the input sequence u . Unlike the forward algorithm, where the intermediate quantities were total probability, here they consist of the maximum probability best_logp[t, k] for the sequence up to time t with final output category k for time t , along with a backpointer to the source of the link. Following the backpointers from the best final log probability for the final time t yields the optimal state sequence.

This inference can be run for the same unsupervised outputs u as are used to fit the semisupervised model. The above code can be found in the same model file as the unsupervised fit. This is the Bayesian approach to inference, where the data being reasoned about is used in a semisupervised way to train the model. It is not “cheating” because the underlying states for u are never observed — they are just estimated along with all of the other parameters.

If the outputs u are not used for semisupervised estimation but simply as the basis for prediction, the result is equivalent to what is represented in the BUGS modeling language via the cut operation. That is, the model is fit independently of u , then those parameters used to find the most likely state to have generated u .

The intercept in this model is \(\alpha / (1 - \beta)\) . An alternative parameterization in terms of an intercept \(\gamma\) suggested Mark Scheuerell on GitHub is \(y_n \sim \textsf{normal}\left(\gamma + \beta \cdot (y_{n-1} - \gamma), \sigma\right)\) . ↩︎

In practice, it can be useful to remove the constraint to test whether a non-stationary set of coefficients provides a better fit to the data. It can also be useful to add a trend term to the model, because an unfitted trend will manifest as non-stationarity. ↩︎

This subsection is a lightly edited comment of Jonathan Gilligan’s on GitHub; see ↩︎

The 35 Best Korean Time Travel Dramas

Korean dramas have captured the hearts of viewers worldwide with their unique storytelling and captivating narratives.

One particular theme that has always had immense popularity is time travel.

These dramas skillfully weave together the complexities of the past, present, and future, inviting viewers on a thrilling journey through time.

The allure of time travel lies not only in the imaginative plotlines but also in the emotional depth it adds to the narratives.

Characters find themselves facing extraordinary circumstances as they grapple with the consequences of altering the course of history or attempting to rectify past mistakes.

This exploration of the human condition, accompanied by a touch of fantasy, creates a profound and thought-provoking viewing experience.

Moreover, K-dramas often infuse time travel narratives with elements of romance, heightening the emotional stakes and adding a layer of the dramatics that we’ve all come to know and love with K-dramas.

The intricate relationships that develop between characters across different time periods lend an air of mystery and longing, making these dramas all the more captivating.

In this article, we will delve into the world of K-dramas that feature time travel! So fasten your seatbelts as we embark on a thrilling journey through time and discover the enchanting realm of Korean dramas about time travel.

Mr. Queen, tvN (2020 – 2021)

Mr. Queen is a historical fantasy drama series that tells the story of Jang Bong-hwan, a womanizing, free-spirited chef working in the Blue House who gets transported back in time and finds himself inhabiting the body of Queen Cheorin in the Joseon dynasty.

Jang Bong-hwan must negotiate the complicated world of the royal court, where political intrigue and power struggles are common while coping with the confusion of being in a woman’s body.

In his role as Queen Cheorin, he is forced to rely on his resourcefulness and uncanny partnerships to survive after becoming embroiled in a plot to depose the king.

Queen and I, tvN (2012)

Queen and I is a fantasy romantic comedy-drama that revolves around Kim Bung-do, a scholar in the Joseon dynasty.

He is also one of the supporters of the rightful Queen In-Hyun, who had previously been deposed by a royal concubine.

A magical talisman somehow transports Bung-do to the present where he encounters the famous Queen In-hyun – who happens to just be a character in a new K-drama – and ends up falling in love with the actress who plays her, Choi Hee-jin.

However, their romance faces numerous challenges and Bong-gu must navigate the complexities of the unknown future, all while striving to find a way back to the past.

Splash Splash Love, MBC (2015)

Splash Splash Love is a Korean time-travel romantic drama that follows the story of Danbi, a high school student who, in a bid to escape a major mathematics exam, uses her time-travel abilities to transport herself to the Joseon era during a rainstorm.

Despite hating math in her own timeline, she becomes known as the smartest in this kingdom!

She and the King inexplicably fall in love, but Danbi makes the hard choice to return to her own time.

King Lee Do swears that he will find her again and once again during a rainstorm, the two are reunited through Lee’s reincarnation.

The King: Eternal Monarch, SBS TV (2020)

The King: Eternal Monarch is a fantasy romance drama that follows the parallel worlds of the modern-day Republic of Korea and the Kingdom of Corea.

Emperor Lee Gon of Corea discovers a portal connecting the two worlds and crosses over to find detective Jung Tae-eul, who is a familiar face associated with his father’s murder.

As they navigate their differences and the political tensions in both worlds, they uncover a dangerous conspiracy and strive to protect their loved ones.

Love blossoms between them, but they face immense challenges, including a power-hungry villain who seeks to exploit the portal for his own gain.

Tale of the Nine Tailed 1938, tvN (2023)

One of the most recently aired series on the list is Tale of the Nine Tailed 1938 , which actually just recently wrapped up its 12th and final episode.

It serves as the second season of the popular supernatural fantasy drama Tale of the Nine Tailed , but the storyline is also somewhat of a prequel.

Following the events with Nam Ji-ah in the original series, protagonist Lee Yeon is transported back in time to 1938 and the then Japanese occupation of Korea.

Dr. Jin, MBC (2012)

Dr. Jin is a historical medical drama that follows the story of Jin Hyuk, a brilliant neurosurgeon in the present day who, because of his highly analytical mind, can sometimes be cold and indifferent.

After suffering from a personal tragedy involving his girlfriend, he gets transported back in time to the Joseon dynasty.

Struggling to adapt to the primitive medical practices of the era, Jin Hyuk uses his modern knowledge to save lives and make advancements in medicine and even medical devices.

He meets people who help him, but his actions soon start interfering with the course of history.

Rooftop Prince, SBS (2012)

In a complex story of intersecting fates and (possible) reincarnations across timelines and generations, Rooftop Prince weaves romance into comical misunderstandings and heartwarming moments.

Crown Prince Lee Gak and his three loyal servants time-travel from the Joseon era to present-day Seoul in a bid to solve the mysterious death of his beloved Crown Princess.

He poses as the lost grandson of a wealthy CEO – who he happens to resemble exactly – and encounters a woman who also looks exactly like his late wife.

Getting closer to her might help solve the mystery of her death back in their timeline, but he may end up hurting someone else along the way.

Live Up to Your Name, tvN (2017)

In the historical medical drama Live Up to Your Name , Heo Im is a respected doctor and acupuncturist in the Joseon era, tending to the poor by day and the nobles by night.

However, he is mistakenly charged with treason and supposedly killed. Instead he wakes up in present-day Seoul!

In the modern era, he meets and forms an unlikely partnership with Choi Yeon-kyung, a pragmatic and ambitious cardiothoracic surgeon.

Together, they navigate the clash between traditional and modern medical practices while overcoming personal obstacles and gradually developing a deep bond.

Faith, SBS TV (2012)

Also known as The Great Doctor , Faith begins in a historical setting where the King’s wife is fatally injured in an accident.

He orders his Captain of the Royal Guard, Choi Young, to enter a portal to retrieve the “Heaven’s Doctor” that they heard of in their legends.

Choi Young travels to modern-day Seoul and brings back Eunsoo, an actual doctor, who then saves the queen.

He had promised to bring her back once her duty was done, but they both soon get embroiled in schemes to keep Eunsoo in the past as more and more people become selfishly interested in her medical prowess.

Life on Mars, OCN (2018)

In a slightly different theme from the previous shows on the list, Life on Mars is a crime thriller series that follows a talented forensic scientist who also leads a crime investigation team.

After an accident, Taejoo awakens to find himself transported back to 1988 with no idea how he got there or why.

Now a detective in the past, he works with his current team to solve a serial murder case with the antiquated technology and methods of the era in order to also solve the mystery behind his time travel.

Signal, tvN (2016)

Signal is yet another crime thriller series that is less about actual physical time travel and more about a mysterious connection between past and present that stems from a seemingly innocuous object: a walkie-talkie.

Park Hae-young is a young criminal profiler from the present day who stumbles upon a mysterious walkie-talkie that helps him solve a murder cold case through its ability to connect him to a detective from the past, Lee Jae-han.

Through the walkie-talkie, they collaborate to solve cold cases and prevent crimes from happening, but consequences soon follow.

Times, OCN (2021)

Similar to Signal , OCN’s political thriller series Times connects the past and present, but this time through a phone and between two journalists : Jinwoo in 2015 and Jungin in 2020.

They work together at first to prevent the murder of Jungin’s father, who is the current president of South Korea.

Along the way, they uncover a dangerous conspiracy involving political corruption and manipulation of time and work together to fight against the forces that seek to control the future so they can preserve their nation’s tomorrow.

Moon Lovers: Scarlet Heart Ryeo, SBS TV (2016)

Moon Lovers: Scarlet Heart Ryeo is a historical drama series based on a Chinese novel.

Like many of the previous shows on this list, the series features the protagonist (named Hajin) traveling way back in time but this time, to the Goryeo dynasty to the year 941.

On top of that, she finds herself in the body of Haesoo, one of the many male princes of the current ruling family who are all fighting for the crown.

As she navigates political rivalries and intense love triangles, Ha-jin discovers her own destiny and the tragic fate that awaits those around her.

Chicago Typewriter, tvN (2017)

In the popular fantasy drama series Chicago Typewriter , viewers will meet three central characters whose stories unfold over the course of time – specifically, two different timelines.

There’s Sejoo, a handsome and renowned writer with many fans who suffers from depression; Seol, a veterinarian and fan of Sejoo; and Jinoh, a talented ghostwriter.

As their paths converge, a mysterious typewriter helps them discover how their past lives were connected during the Japanese occupation of Korea in the 1930s , and how their friendship then crossed decades into the present.

Manhole, KBS2 (2017)

If we could time travel, we’d always be tempted to go back in time to prevent the things that we don’t want to happen.

In Manhole , Bongpil does just that when he discovers a manhole that allows him to travel back in time.

What does he do when he gets to the past? Well, anything to stop his dream girl’s wedding from happening!

However, his efforts often lead to hilarious and chaotic consequences.

Alongside his loyal friends, he navigates the challenges of altering the past and learns valuable lessons about love, friendship, and the importance of living in the present.

Hit the Top, KBS2 (2017)

Hit the Top is a combination of humor and nostalgia, and takes place primarily in the constantly evolving world of K-pop.

Back in the early 1990s, popular K-pop idol Hyunjae accidentally time travels to 2017, where he soon discovers that he had been in the news for mysteriously disappearing and subsequently presumed dead.

As he adjusts to his vastly different life in the future, he tries to investigate the cause of his time travel.

At the same time, an aspiring K-pop trainee crosses his path – one who he is more connected with than either of them expected.

My First Love, OCN (2018)

The South Korean romantic fantasy drama My First Love centers on Kang Shinwoo, a man in his 30s who goes back in time to his high school years.

Shin-woo runs into his younger self and his first love, Han Jisoo, when he travels back in time. Shin-woo makes an effort to rewrite their fate and win Jisoo’s affection in an effort to alter the tragic course of their history.

At the same time, Shinwoo discovers important lessons about love, regret, and the value of honoring the present as he wrestles with the repercussions of changing the past.

Tomorrow, with You, tvN (2017)

Tomorrow, with You is a romance series that follows the story of Yoo Sojoon, a time-traveling CEO of a real estate agency who can foresee his future through subway rides, and his wife Marin, a cheerful and optimistic amateur photographer.

To change his impending tragic fate that he accidentally glimpses, he marries Marin and together, they navigate the complexities of marriage while battling against their own personal struggles.

Eventually, Sojourn learns to truly love her amidst the consequences of altering destiny.

Marry Him If You Dare, KBS (2013)

Yet another romance drama on the list is KBS’ Marry Him If You Dare , which is set in the world of television broadcasting.

It follows the story of Na Mirae, a young woman who receives a mysterious opportunity to travel back in time to change her future.

The present Mirae is married to news anchor Kim Shin but travels to visit her 32-year-old self to prevent that from happening.

This causes her to go down a completely different path to really pursue the dreams she had always wanted.

Familiar Wife, tvN (2018)

The decisions we make impact the road our lives go down; one different decision could catapult things in a direction we never would have expected.

That’s what happens in Familiar Wife , a romantic fantasy drama that tells the story of Joohyuk, a married man who regrets the choices he made in his life.

One day, he encounters a mysterious opportunity to travel back in time and change his past.

In the blink of an eye, the life he knew with his wife Woojin and best friend Joonghoo is gone and his first love Hyewon comes back into the picture.

Go Back Couple, KBS2 (2017)

It’s complicated enough when a person goes back in time to change their previous decisions, but what happens when two people time travel together? Especially when these two people have a life together in the present?

Go Back Couple follows married couple Bando and Jinjoo, who are parents to a young boy and have become deeply unsatisfied with their lives – family, career, and relationship-wise.

Suddenly, the couple is transported 18 years back to when they were university students, where they make different choices and learn the enduring power of love.

The Light in Your Eyes, JTBC (2019)

The Light In Your Eyes is also known as Dazzling  and emphasizes the importance of valuing the people who cause the lights in our eyes to shine throughout our lives.

In the drama series, we meet a young woman named Hyeja who possesses a special watch that allows her to manipulate time. However, using the watch causes her to age rapidly.

After having used it one too many times, Hyeja is now passing the time at an elderly care center where she meets Joonha, an employee who looks like the young Hyeja’s husband.

Nine: Nine Time Travels, tvN (2013)

A news anchor named Park Sunwoo finds nine magical incense items that enables him to travel 20 years back in time in the fantasy thriller drama entitled Nine: Nine Time Travels .

Sunwoo makes numerous trips to the past in an effort to change the tragic events that have occurred in his life, changing both his decisions and the course of history.

He soon learns, though, that every change has unintended consequences both for himself and those he loves around him, and he is forced to deal with the moral dilemmas that follow.

Sisyphus: The Myth, JTBC (2021)

In the dystopian science fiction series Sisyphus: The Myth , engineer and brilliant quantum physicist Taesul crosses paths with a mysterious woman from the future named Seohae.

In Seohae’s present, she explores a war-torn Korea and travels back in time to both prevent Taesul’s death and his invention of the time travel device she used.

Meanwhile, Taesul is trying to investigate the strange circumstances around his brother’s death and a time-travel conspiracy where he finds himself embroiled in a dangerous game where past, present, and future collide.

Tunnel, OCN (2017)

Some crimes can span decades, with the case passing through the hands of several teams and detectives without ever getting resolved.

In the crime thriller Tunnel , we begin the story in 1986 when Detective Park Gwangho is hot on the tail of a serial killer.

He chases the perpetrator through a tunnel and ends up in 2016 where the same killer is still on the loose and just beginning to continue his murder spree.

Gwangho teams up with Detective Kim Seonjae and criminal profiler Shin Jaeyi to race against time and apprehend the killer before they can commit further crimes.

Alice, SBS TV (2020)

Science fiction drama series Alice weaves a complex tale of love, fate, and the consequences of tampering with time.

The title is a reference to the time-traveling agency in 2050 that sends clients to the past in order to find peace and closure with dead loved ones and also makes sure these trips are done properly.

Viewers meet Alice agents Taeyi and Minhyuk as they travel almost 60 years to the past on a mission, but their personal lives get intertwined and affect the present – 2020.

My Perfect Stranger, KBS2 (2023)

My Perfect Stranger is one of the newest shows on this list, having just finished airing in June of this year!

The romance mystery tells a beautiful story of two strangers who time travel back to 1987.

Haejun is a seemingly aloof newscaster who is trying to seek the truth behind a serial killing case; Yoonyoung is an editor at a publishing company who wants to prevent the marriage of her parents.

They find themselves in the past and also discover that their goals are more intertwined than they think.

Kairos, MBC TV (2020)

Kairos is similar to a few other shows as the time travel occurs in parallel timelines that connect characters instead of actually traveling through time.

However, it is also a rare one as the timelines are only one month apart!

Seojin is a company executive whose daughter suddenly goes missing while Aeri is a young woman living one month behind and whose mother also disappears.

When Seojin and Aeri meet through a phone call, he asks the latter to help find his daughter while helping to find her mother in his future timeline.

God’s Gift: 14 Days, SBS (2014)

God’s Gift: 14 Days tells the story of Kim Soohyun, a distraught mother whose daughter is abducted and killed, who miraculously gets the chance to travel back to the two weeks prior to the tragedy.

Soohyun is in a race against time to find the kidnapper and stop terrible things from happening because she is determined to change the outcome.

She discovers that the truth is much more complicated and perilous than she had anticipated as she digs deeper into the murky secrets surrounding her daughter’s death.

365: Repeat The Year, MBC TV (2020)

If you could repeat the last year of your life, would you?

In the mystery thriller 365: Repeat The Year , 10 people are given the opportunity to reset their lives by going back one year in time.

They participate in a time-traveling experiment, hoping to change their fates and undo their regrets.

However, they soon realize that altering the past has dire consequences, and they must unravel the mystery behind the experiment to save their lives.

Reunited Worlds, SBS (2017)

In Reunited Worlds , a man named Sung Haesung mysteriously reappears 12 years after his untimely death.

While he hasn’t aged, he finds that everyone around him has changed and gone on with their lives.

Haesung reconnects with his childhood friends, including his first love Jung Jungwon, as he navigates the difficulties of his new reality.

Together, they learn the truth about his time travel and work to mend relationships, sever old wounds, and find happiness in the present.

See You in My 19th Life, tvN (2023)

See You in My 19th Life is based on the webtoon of the same name that was published on Naver.

It tells the story of Jieum, who has lived out several lifetimes through reincarnation and has the supernatural ability to remember all of her past lives.

Her 18th life is tragically cut short due to an accident, and as she lives out her 19th, she knowingly heads down a path that will lead her to reconnect with a man from her previous life.

A Time Called You, Netflix (2023 – present)

Then there is the romance drama A Time Called You , which premiered on Netflix relatively recently – on September 8th, 2023.

It is an adaptation of a Taiwanese series and follows Junhee, who continues to mourn her boyfriend Yeonjun’s death one year later.

Somehow, she finds herself transported back to 1998 and in the body of an 18-year-old named Minju, who meets Siheon, a boy who looks like her deceased partner.

Marry My Husband, tvN (2024 – present)

The newest series on this list is Marry My Husband , which premiered on tvN on January 1st, 2024.

Marry My Husband follows Jiwon, described as a “time-limited woman” who has the opportunity to go back ten years.

In her new present, she dreams of exacting revenge on her cheating, murderous husband who killed her after having an affair with her best friend.

The Story of Park’s Marriage Contract, MBC (2023 – present)

Finally, we come to the last show in this countdown – The Story of Park’s Marriage Contract .

The fantasy romance series tells the story of Park Yeonwoo, a beautiful woman from the Joseon dynasty who loses her husband on the first night of their marriage.

She suddenly finds herself transported to the 21st century and under a modern marriage contract with Kang Taeha, who looks exactly like her husband.

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Hi, thanks for the list. There is also “Perfect marriage revenge” 2023

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Mark Cobden is sentenced to prison after accidentally killing a man. Consumed by his guilt, he accepts his sentence but is quickly confronted with the ruthless realities of life inside prison.

• Cast: Sean Bean, Stephen Graham, James Nelson-Joyce

Mark Cobden is sent to prison and has to learn quickly how to survive. When an inmate identifies prison officer Eric McNally’s weakness, he faces an impossible choice.

EPISODE 1 • 56m

Mark is being bullied by fellow inmate Johnno. Does he tell the prison officers and be labelled a grass or risk the attacks becoming more and more violent? Eric’s son is attacked.

EPISODE 2 • 59m

Mark suffers a tragic personal loss and is given the chance to leave prison for a day. Eric is forced to take greater risks to protect his family. But where will he draw the line?

EPISODE 3 • 67m

Stephen Graham

James nelson-joyce, nabil elouahabi, natalie gavin, hannah walters.

First look at Travis Fimmel in Season 2 of the critically acclaimed crime drama Black Snow

The Stan Original Series Black Snow is officially returning for Season 2, with Vikings actor Travis Fimmel at the helm once again.

And now,  Stan has dropped some epic first-look images of the new season which is currently in production in Queensland.

Stream Season 1 of Black Snow now, only on Stan.

Fimmel and Jana McKinnon from Stan Original Series Bad Behaviour headline the cast of the six-part mystery-drama.

Produced by Goalpost Pictures and set against the stunning backdrop of the Glasshouse Mountains, the series sees Fimmel returning as Detective Cormack, now facing two intense missing persons cases.

In the upcoming season, Detective Cormack navigates both a professional investigation into the disappearance of Zoe Jacobs (played by McKinnon) and a personal quest to find his long-lost brother.

Season 1, set in a small town in North Queensland, told the story of the murder of 17-year-old Isabel Baker.

The crime shocked the small town of Ashford and devastated Isabel's Australian South Sea Islander community. The case was never solved, the killer never found, until the opening of a time capsule unearthed a secret that put cold-case detective James Cormack (Fimmel) on the trail of the killer.

READ MORE: Jana McKinnon on why so many will relate to the dark new Stan Original series Bad Behaviour

Fimmel himself shared his pride in the strength of Australian film and television, emphasising the global recognition these productions now receive.

He said: "I think Australian film and television has never been stronger. Now, with the addition of streaming networks and their reach, I'm so proud that our local productions and their hard work can be recognised not only here, but around the world.

"I'm excited to be a part of another season of Black Snow and showing off what Australians have to offer."

"That, and I'm happy to have free food and a job," the actor added.

Stan also announced that Black Snow 's second season will star Megan Smart, Alana Mansour, Dan Spielman, Victoria Haralabidou and Underbelly and Five Bedrooms star Kat Stewart.

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Stan's Chief Content Officer, Cailah Scobie, expressed excitement about the new season saying: "Following the enormous success of the first season of Black Snow we are thrilled to be working with the team at Goalpost Pictures and the incredible cast, led by Travis, on another gripping season.

"We thank our partners at Screen NSW, Screen Queensland, the City of Gold Coast and All3Media and look forward to sharing the new season with audiences soon."

Executive Producer Rosemary Blight echoed the sentiment, expressing delight in returning to Queensland for filming and delivering another captivating season to audiences worldwide.

READ MORE: First look at Stan's new crime thriller series Hotel Cocaine

The series has garnered international acclaim, with Screen Queensland and Screen NSW both proud to support its production and recognize its contribution to the local industry.

With seasoned directors and producers at the helm, including Sian Davies, Rosemary Blight, and Lucas Taylor, along with new talent like director Helena Brooks, the series promises to maintain its high standards.

The new season of Black Snow , produced in collaboration with All3Media International and Sundance Now, receives essential backing from Screen Queensland, Screen NSW, and the City of Gold Coast, ensuring its continuation as a top-tier television production.

The brand new season of the Stan Original Series Black Snow ​ is currently in production.

Every episode of Season 1 is now streaming, only on Stan.

Nine Entertainment Co (the publisher of this website) owns and operates the streaming service Stan .

Property News: This five-bedroom, six-bathroom home at 671 Lincoln Avenue in Winnetka, Illinois is very familiar. Take a look at the house from Home Alone that is for sale.

David Cronenberg's Star Trek: Discovery Role Explained - Who Is Doctor Kovich?

Contains spoilers for "Star Trek: Discovery"

The universe of "Star Trek" is so jam-packed full of fascinating characters — human and otherwise — that there's always tons of information we don't know about them, even the recurring ones. Someone like Dr. Kovich (played by the esteemed horror director David Cronenberg), for example, has been making his presence felt over the last three seasons of "Star Trek: Discovery," but for the most part, very little has been revealed about his actual character.

While this might be frustrating for lore-hungry fans, it has also allowed him to remain a tantalizingly mysterious figure whose true intentions are always unclear. Until now, that is. In the series finale of "Star Trek: Discovery," we finally get an explanation of who Dr. Kovich really is — and the reveal is a lovely gift to fans of "Star Trek: Enterprise." Because Dr. Kovich isn't just Dr. Kovich, the chaotic sometimes-ally of Captain Michael Burnham (Sonequa Martin-Green) and Starfleet officer whose job operates in a cloud of secrets: He's also Agent Daniels, a temporal operative featured in "Enterprise."

Who is Agent Daniels?

For a long time now, "Star Trek" has had a complicated history with the concept of time travel. While sometimes it's been used as a band-aid of sorts, to cover up any inconsistencies between the many different arms of the franchise, that doesn't mean that there aren't any internal rules about messing with time. Thus, different "Star Trek" series have employed the use of temporal agents, whose job is to maintain the integrity of crucial timelines, even if that means going into the past themselves and cajoling key figures into making the right choices.

It's in this capacity that we first meet Agent Daniels on "Star Trek: Enterprise." Originally played by Matt Winston, he made his first appearance back in 2001 in the Season 1 episode "Cold Front," and appeared several times over the first four seasons of the show. He would often turn up to resolve some time-related crisis or to nudge Captain Jonathan Archer (Scott Bakula) into aiding him in his efforts to preserve the timeline. In a landscape where many of the frontiers of space are being mapped out, Daniels was a reminder that time (perhaps the final  final  frontier) still presents its own mystery. His return as Dr. Kovich is a pleasant surprise, especially to "Enterprise" fans, and adds to the satisfying conclusion of the "Star Trek: Discovery" finale that capped off a stellar final season.