Time Series

General Time Series learning resources and papers, uncategorized

• Introduction to Time Series Forecasting with Python - How to Prepare Data and Develop Models to Predict the Future, by Jason Brownlee [ book ] This is my favorite practical approach to an introduction to Time Series. It reads fast, maybe one week-end and you're done. Topics discussed: framing TS as a supervised learning problem, basic feature engineering, visualization, resampling, interpolation, power transforms, moving average smoothing, random walk, structural decomposition, stationarity, autoregression, autocorrelation.
• Practical Time Series Methods - a short series of instructional notebooks on Kaggle, by Konrad Banachewicz [ jupyter notebooks ] [ youtube videos ] I'm a fan of Konrad's style. He's casual and makes extensive use of humor, while delivering information dense learning capsules. He has extensive experience on this topic and can answer any qestion from the audience. The topics discussed are:

  • Part 0 - the basics

  • Part 1a - smoothing methods

  • Part 1b - Prophet

  • Part 2 - ARMA

  • Part 3 - Time series for finance

  

  The notebooks series is accompompanied by video tutorials in collaboration with Abhishek Thakur published on his YouTube channel:

  • Talk 0 - based on part 0 of the series

  • Talk 1 - combining the content from parts 1a and 1b

  • Talk 2 - based on part 2 notebook

  • Talk 3 - based on part 3

  
• Time Series: Theory and Methods (Springer Series in Statistics), by Peter J. Brockwell, Richard A. Davis [ book ]
• Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics) 3rd ed. 2011 Edition, by Robert H. Shumway (Author), David S. Stoffer [ book ]
• Time Series Analysis by State Space Methods: Second Edition (Oxford Statistical Science Series), by James Durbin (Author), Siem Jan Koopman (Author) [ book ]
• Forecasting: Principles and Practice (3rd ed), by Rob J Hyndman and George Athanasopoulos [ book ] The is freely available on the author's website, through the link above. Rob J Hyndman published a lot more resources (book, scientific articles, opinions, code) - see his website
• Time Series Analysis Lecture Notes, by Ross Ihaka Statistics, Department University of Auckland. [ book ] Freely available on the link above.
• Deep Learning for Time Series Forecasting - Predict the Future with MLPs, CNNs and LSTMs in Python, by Jason Brownlee [ book ] As everything else by Jason Brownlee, the book is filled with step-by-step practical examples and projects in Python (Keras and TensorFlow 2).