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  • Mastering Modern Time Series Forecasting: A Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in Python

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Mastering Modern Time Series Forecasting: A Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in Python

by Valery Manokhin PhD (Author) Format: Paperback
5.0 out of 5 stars (2)

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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.
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  1. ISBN-10
    1919465839
  2. ISBN-13
    978-1919465838
  3. Publication date
    March 24, 2026
  4. Language
    English
  5. Dimensions
    7 x 1.79 x 10 inches
  6. Print length
    762 pages
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Product details

  • Publisher ‏ : ‎ North Star Academic Press
  • Publication date ‏ : ‎ March 24, 2026
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 762 pages
  • ISBN-10 ‏ : ‎ 1919465839
  • ISBN-13 ‏ : ‎ 978-1919465838
  • Item Weight ‏ : ‎ 3.99 pounds
  • Dimensions ‏ : ‎ 7 x 1.79 x 10 inches
  • Best Sellers Rank: #130,256 in Books (See Top 100 in Books)
  • Customer Reviews:
    5.0 out of 5 stars (2)

About the author

Follow authors to get new release updates, plus improved recommendations.
Valery Manokhin PhD

Valery Manokhin PhD

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Full content visible, double tap to read brief content.

Valery Manokhin, PhD, MBA, CQF.

No hype, only rigour. Valery writes for readers who know the basics and want depth, mathematical clarity, and working implementation.

He holds a PhD in machine learning from Royal Holloway, University of London, supervised by Professor Vladimir Vovk, a founder of conformal prediction. Through Vovk, whose PhD advisor was Academician Andrei Kolmogorov, Valery is two steps from Kolmogorov in the direct academic lineage: Kolmogorov number 2. He also holds an MBA from Warwick, an MSc in Computational Statistics and Machine Learning from UCL, and the CQF.

His ML books include Mastering Modern Time Series Forecasting and Applied Conformal Prediction: rigorous applied references on forecasting and distribution-free uncertainty quantification with finite-sample guarantees.

He also translates classic Russian mathematics textbooks into faithful English editions.

Readers in 100+ countries.

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