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249700713
Mastering Modern Time Series Forecasting: A Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in Python
Item #:
249700713
MVR 3101
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The most comprehensive guide to time series forecasting ever published — 730 pages covering every major statistical, machine learning, and deep learning model in Python.
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- The most comprehensive guide to time series forecasting ever published — 730 pages covering every major statistical, machine learning, and deep learning model in Python.From classical ARIMA and exponential smoothing to N-BEATS, PatchTST, and foundation models, this book gives you the theory, the code, and the practical wisdom to forecast any time series.What makes this book different:Forecastability-first approach. Before building a single model, Chapter 2 teaches you to measure whether your series can be forecast — using 17 metrics no other book covers. This chapter alone is the most comprehensive treatment of forecastability in any commercial forecasting book.Every major model family, in depth. ARIMA and its variants. All 30 ETS configurations. Gradient-boosted trees (LightGBM, XGBoost, CatBoost). Deep learning architectures: LSTMs, DeepAR, N-BEATS, N-HiTS, TSMixer, TiDE, and TimeMixer. Transformer models: PatchTST, TimeXer, Crossformer, and TFT. Foundation models: Chronos, TimesFM, Moirai, and TimeGPT.Production-grade feature engineering. A 100-page chapter on feature engineering covers lag features, rolling statistics, spectral analysis, entropy measures, embedding methods, and chaos-theoretic features — with code for every technique.Rigorous performance evaluation. Two dedicated chapters on forecast metrics and state-of-the-art evaluation methodology, including proper cross-validation, statistical testing, and calibration.Implementations in Python, R, Julia, and Rust. While Python is the primary language, key models include implementations in R (forecast, fable), Julia (StateSpaceModels), and Rust (augurs, OxiDiviner) for production deployment.Who this book is for:Data scientists, ML engineers, quantitative analysts, researchers, and anyone who needs to forecast time series professionally. Assumes familiarity with Python and basic statistics.About the author:Valery Manokhin, PhD, is the author of multiple bestselling books on forecasting and machine learning. His work has been cited in academic research worldwide.
| Publisher | North Star Academic Press |
| Publication date | 24 Mar. 2026 |
| Language | English |
| Print length | 762 pages |
| ISBN-10 | 1919465839 |
| ISBN-13 | 978-1919465838 |
| Item weight | 1.81 kg |
| Dimensions | 17.78 x 4.55 x 25.4 cm |
Product Description
Mastering Modern Time Series Forecasting: A Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in Python
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What prior knowledge is required to use this book?
Answer: A familiarity with Python and basic statistics is assumed. -
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Are there practical examples provided in the book?
Answer: Yes, the book includes code and practical examples for each model and technique. -
Question:
Is this book suitable for beginners?
Answer: While suitable for all levels, it is primarily targeted at professionals needing to forecast time series.
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MVR 3101
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Features & Benefits
- Covers every major forecasting model in 730 pages.
- Includes classical and modern methods like ARIMA, N-BEATS, and Transformer models.
- Forecastability-first approach with exclusive metrics for measuring forecastability.
- In-depth chapters on feature engineering and performance evaluation.
- Implementations available in Python, R, Julia, and Rust.
- Ideal for data scientists, ML engineers, and quantitative analysts.
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