£111.69

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

Languages

Price data last checked 30 day(s) ago - will refresh soon

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

It has never been this cheap. We have no record of a lower price.

£112 today · cheaper than every other day in the last 1 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 15 days • 15 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 15 days • 1 price levels

Days at Price

Price Analysis

Most common price: £112 (15 days, 100.0%)

Price range: £112 - £112

Price levels: 1 different prices over 15 days

Description

The most comprehensive guide to time series forecasting ever published — 782 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 creator of the most popular course on conformal prediction (5,000+ students) and author of multiple bestselling books on forecasting and machine learning. His work has been cited in academic research worldwide.

Product Specifications

Format: paperback
ASIN: 1919465839
Category: Books > Subjects > Computing & Internet > Programming > Languages
Domain: Amazon UK
Release Date: 24 March 2026
Listed Since: 25 March 2026

Barcode

No barcode data available

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

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Manning Time Series Forecasting Using Foundation Models Book

Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR

Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Wiley Statistical Methods for Forecasting - Probability & Statistics

Introduction to Time Series Analysis and Forecasting (Wiley Series in Probability and Statistics)

Business Forecasting: A Practical Approach

Forecasting in Business and Economics (Economic Theory, Econometrics, and Mathematical Economics)

Forecasting for Economics and Business (The Pearson Series in Economics)

Principles of Forecasting: A Handbook for Researchers and Practitioners: 30 (International Series in Operations Research & Management Science, 30)

Time Series Forecasting in Python

Advanced Time Series Data Analysis: Forecasting Using EViews

Handbook of Economic Forecasting (Volume 1)

Judgment in Predictive Analytics: 343 (International Series in Operations Research & Management Science, 343)

Introduction to Time Series Analysis and Forecasting (WILEY SERIES IN PROB & STATISTICS/see 1345/6,6214/5)

Handbook of Economic Forecasting, Vol 2A: Volume 2a

Forecasting and Market Analysis Techniques: A Practical Approach

Forecasting, Time Series, and Regression (with CD-ROM)

Introduction to Time Series Analysis and Forecasting

Advances in Business and Management Forecasting: 3

Forecasting in Business and Economics (Economic Theory, Econometrics, and Mathematical Economics)

Handbook of Economic Forecasting, Vol 2B: Volume 2b

Time Series Analysis and Forecasting by Example

Business Forecasting with ForecastX Software & Student CD

Advances in Business and Management Forecasting: 4