Prediction is hard -- now I don't have to write this post myself. Just read @tslumley:https://t.co/dKRnClwKPa #rstats
— Rob J Hyndman (@robjhyndman) May 7, 2020
Prediction is hard -- now I don't have to write this post myself. Just read @tslumley:https://t.co/dKRnClwKPa #rstats
— Rob J Hyndman (@robjhyndman) May 7, 2020
The "cubic model" from @CEA + Kevin Hassett was pretty clearly fit on log(deaths + 1). Which is... pretty dangerous for forecasting.
— David Robinson (@drob) May 5, 2020
Just imagine if they'd fit a quartic model pic.twitter.com/VTlFoD9qvC
Neural forecasting: Introduction and Literature Overview [66pp]
— ML Review (@ml_review) April 23, 2020
By Amazon Research @LoVVgE @canerturkmen @lostella
Concise Deep Learning introduction through the prism of Forecasting.https://t.co/Kq6PIj5TNe pic.twitter.com/2R3TK2EeKz
Time series forecasting best practices from @Microsoft, with notebooks in #Python and R #rstats https://t.co/0qA9fOQien
— David Smith (@revodavid) April 7, 2020
Difference Attention Based Error Correction LSTM Model for Time Series Prediction
— Thomas (@evolvingstuff) March 31, 2020
"difference-attention LSTM model and error-correction LSTM model are respectively employed and combined in a cascade way"https://t.co/JwyttchDuq pic.twitter.com/U6bEwTvs6t
The M5 Forecasting Competition has been launched hosted by @kaggle. It consists of two tracks. To register in Forecasting-Accuracy use https://t.co/GILfO9LTKB for Forecasting-Uncertainty use https://t.co/uikxjf7XWJ There are 1500 registered in the first 12 hours since its opening
— Spyros Makridakis (@spyrosmakrid) March 3, 2020
Announcing the M5 Competition: Start March 2, end June 30, consists of 43194 hierarchical sales time series, including explanatory variables made available by Walmart. Run through Kaggle's platform. Prizes to exceed $100K. @nntaleb the advisor for evaluating Forecasts/Uncertainty pic.twitter.com/XOLeIvbWxy
— Spyros Makridakis (@spyrosmakrid) December 23, 2019
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 [68pp]https://t.co/QKrAQJolhE pic.twitter.com/moPbkqfqzq
— ML Review (@ml_review) December 4, 2019
Some more information about the M5 Competition. It will start on the 2nd of March, 2020 and end the 30th of June 2020 It will be run using the @Kaggle Platform. There will be about 100K time series of sales data made generously available by @Walmart as shown in the attached Table pic.twitter.com/cB59xI2WL7
— Spyros Makridakis (@spyrosmakrid) November 28, 2019
Time series prediction is clearly a very important problem for @UberEng. Here is a blogpost and a couple of papers from their presentation at #MLconfSF: https://t.co/YgZ7ey7Nfo https://t.co/DinOPvlPJR https://t.co/GK1D8TDa8F
— Xavier Amatriain (@xamat) November 8, 2019
Non-Gaussian forecasting using the new fable package: https://t.co/18A3jL8rFV #rstats pic.twitter.com/VzGjNmzkhY
— Rob J Hyndman (@robjhyndman) October 17, 2019
The fable package is now on CRAN. Here is a brief intro to the main functions. https://t.co/75vf1T2jVU #rstats #forecasting Thanks to @mitchoharawild for the package.
— Rob J Hyndman (@robjhyndman) September 30, 2019