Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition) Paperback – 15 Oct. 2021

Very good concise starting point for time series. It’s light on theory but the author makes this very clear in the introduction. This is a non-nonsense practical book, and assumes competence in python. The writing style is a bit terse in places but understandably so (given the author’s background), even so - the text is direct and clear. Liked that the author goes back to basics in code so that concepts are not just assumed or obscured by package calls (e.g., tensors, trend removal, alternate models). The more involved model descriptions (e.g., RNN/LSTN/TCN), can be a quite brief, you need to read between the lines, look at other sources, and work through the code.There is less coverage on more advanced topics, confidence intervals, regular and irregular timestamps, complex correlations, optimisations. The author could have cited deeper material and summarised gaps between where this code stops and real-world solutions start, That said, this is a solid starting point for time series. Another 100 pages would have really pushed this up a notch and been equally enjoyable to read. Nice one.