Deep Learning for Time Series Cookbook: Use PyTorch and Python Recipes for Forecasting, Classification, and Anomaly Detection

Time-series work often begins with practical questions: how should the data be prepared, which model suits the task, and how can its results be evaluated? Deep Learning for Time Series Cookbook answers through hands-on Python recipes that apply deep learning to forecasting, classification, and anomaly detection, with PyTorch at the center of its toolkit.

Vitor Cerqueira and Luís Roque build from time-series fundamentals toward more advanced neural architectures. Recipes explain not only what to implement, but how the approach works—useful context when adapting examples to a dataset of your own.

Start with the shape of the data

The opening material covers practical preparation and exploration, including loading and visualizing series, resampling, handling missing values, decomposing data, examining autocorrelation, and working with multivariate observations. These foundations help readers understand the structure and quirks of time-series data before modeling begins.

Build forecasting models in stages

The book moves from simple forecasting approaches and ARIMA to neural-network methods such as feedforward networks, LSTMs, GRUs, and CNNs. It then develops multivariate and global forecasting approaches, with recipes addressing multi-step predictions, multiple series, and model evaluation.

Along the way, readers encounter ways to handle common data characteristics such as trend, seasonality, missing values, and changing variance. The progression makes it easier to compare modeling choices rather than treating a single architecture as the answer to every forecasting problem.

Explore advanced architectures

Later recipes introduce techniques and tools including N-BEATS, DeepAR, Transformers, and Temporal Fusion Transformers, alongside PyTorch-focused forecasting libraries. The book also addresses classification and anomaly detection, extending its scope beyond predicting future values.

A practical reference for Python practitioners

Its recipe format suits data scientists, machine-learning practitioners, and technically minded readers who want implementation-oriented guidance for time-series problems. Familiarity with Python and machine learning will help readers make the most of the code and explanations.

For a project involving sequences, forecasts, or unusual patterns, this cookbook offers a structured way to explore established methods and modern deep-learning approaches.