Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas 2nd ed. Edition

This book is amazing. I recommend it.

I have some financial clients that prefer I use python for their projects. This book came in handy when they needed me to crunch some numbers in their tick data. I like how it presented the information in layered approach that made it simple for me to understand and actually deliver something to my clients.Highly recommend to pick this up if you need to work with time series data with Python.

I couldn't have made it as far as I have now without this book. Invaluable!

Time series forecasting was got me interested in AI. Yes, LLMs are cool and all, but I see the ability to make predictions as much cooler. And I entered into a few machine learning contests for fun and ended up making some money.But I never had a book like this. I probably would have done a lot better with it. So whether you need it for your job, you're just curious, or maybe you want to enter some Kaggle contests, this book will be a big help. Time series forecasting is used in weather forecasting, sales forecasting, stock price forecasting, fraud detection, and more. And this book not only introduces you to the fundamental time series concepts and the latest techniques to make your own predictions, but also real-world examples of time series forecasting.

One major, organizational AI integration goal seeks better vision and understanding by finding patterns, and forecasting trends humans may miss. “Modern Time Series Forecasting with Python, 2nd edition” (Packt, 2025) by Manu Josepth and Jeff Tackes provides a step-by-step reference to building efficient forecasting. This exhaustive tome covers foundational math, algorithm development, and effective coding to achieve useful results. Learning is divided in four sections: a basic time series approach, machine learning fundamentals, deep learning principles and the mechanics of forecasting. Recommend this as a desk reference for anyone working with these challenging solutions.Beginning with time series studies mirrors basic data approaches in many areas. The first step begins with understanding the overall goal by determining which data will be studied and how one intends to forecast. Once the strategy is set, one needs to acquire and process data and determine visualization methods. After all, if I can not see the AI conclusions, it will not do any good. Finally, one needs to recognize baselines. Baselines determine variability, even with forecasting, to help determine when changes occurs and if those changes matter.After presenting the basics, the next step exhaustively dives into establishing different interactions with ML tools for time series outputs. One key element here examines stationarity within a time series, the ability to determine whether statistical references remain consistent over time. These measures allow determining if characteristics such as mean, variance, and other aspects connect rationally. For example, if studying temperatures, and the average mean usually only moves a couple of degrees over a day, a much wider swing could suggest either issues in the data, or an underlying condition. The section also discusses comparing multiple time series, increasing variables by comparing elements not initially connected, such as using local temperature, power usage, and then comparing to employment statistics within an area.The deep learning section presents solution for the true expert and may be a little thick if your own experience is lacking. Deep learning has become possible by compute and data increases in hardware and software over the recent years. These solutions require multiple models involving recurrent neural networks and establishing weights for measured factors. The latter chapters in this section provide some excellent strategies as well as a comprehensive review of common deep learning architectures. Comparing the options early, and matching to a strategy can be highly beneficial if you launch your own applications.The last section ties everything together with a look at how the comprehensive models from early can be applied in a forecasting environment. The authors cover multistep forecasting when one model provides answers to another model for a subsequent forecast, extremely valuable in blending multiple local solutions into wider, global conclusions. Not content to leave the work at just forecasting, the book wraps up with discussing validating ML models, and metrics demonstrating common errors. I always love metrics, and enjoyed reviewing the intrinsic and extrinsic metrics common to ML frameworks.I enjoyed the entire book; if I had one complaint, it ran a little long at almost 700 pages. The authors could have easily split the material into two books, one on time series with Python, and a second on deep learning models. Granted, having all the tools in one place is excellent for a reference, but can take quite some time during a casual read. This is a second edition, so the added material likely expanded the initial volume.Overall, “Modern Time Series Forecasting with Python, 2nd Ed” was a truly comprehensive look at time series solutions, initial ML builds, incorporating deep learning solutions, and measuring success. The book covered the initial math at every step, suggested use cases, provided coding samples, and linked to a GitHub for further learning. If you are working with any time-series solutions, I’d purchase this book and keep it on your desk.

I am a Principal Data Scientist and have actively worked in various ML problems. Time series forecasting is a challenging problem by nature. Many approaches are used in day to day research and work to tackle forecasting problems in business. This book is an absolute must for every data scientist for constant brushing up the concepts in Time series forecasting or beginners who want to have a deeper understanding of this subject.The authors have done an exhaustive research to write this book. This book is organized into 3 sections or parts. The first part deals with data generation process, looks into seasonality, outliers, baseline model generation and issue of missing values. The approach is based on traditional approaches like ARIMA, AutoARIMA, LOESS, spectral entropy and Kaboudan metric.The second part approaches the problem of time series forecasting based on regression model like Linear Regression, Decision Trees Regressor, Random Forest Regressor, Light GBM Regressor.The final part of this book focuses on Deep learning methods for Time Series forecasting. A brief detour is taken to explain concepts about Neural Network. Recurrent Neural Network architecture is used to implement forecasting. The author also focusses on LSTM, GRU. Recent methods in deep learning, like attention mechanism is also touched. The book ends with probabilistic model.This is definitely a one stop book for learning everything about time series forecasting. You don't have to purchase any course particularly related to understanding Time series modeling worth 100s of dollars if. you follow this book. This book should be in the collection of every data scientist's and student's eager to learn about time series forecasting.