Time Series Analysis with Python Cookbook : Practical Recipes for the Complete Time Series Workflow, from Modern Data Engineering to Advanced Forecasting and Anomaly Detection.

Format:

Book

Author/Creator:

Atwan, Tarek A.

Language:

English

Subjects (All):

Time-series analysis.

Forecasting.

Physical Description:

1 online resource (813 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2026.

Summary:

Perform time series analysis and forecasting confidently with this Python code bank and reference manual.Access exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations.Purchase.

Contents:

Cover

Title Page

Copyright Page

Contributors

Table of Contents

Preface

Free Benefits with Your Book

Chapter 1: Reading Time Series Data from Files

Technical requirements

Reading data from CSV and other delimited files

Reading data from an Excel file

Reading data from URLs

Reading data from Parquet files

Chapter 2: Reading Time Series Data from Databases

Reading data from a relational database

Reading data from Snowflake

Reading data from MongoDB

Chapter 3: Persisting Time Series Data to Files

Serializing time series data with pickle

Writing to CSV and other delimited files

Writing data to an Excel file

Storing data in cloud storage (AWS, GCP, and Azure)

Chapter 4: Persisting Time Series Data to Databases

Writing time series data to a relational database

Writing time series data to MongoDB

Writing time series data to Snowflake

Chapter 5: Working with Date and Time in Python

Working with DatetimeIndex

Providing a format argument to DateTime

Working with Unix epoch timestamps

Working with time deltas

Converting DateTime with time zone information

Working with date offsets

Working with custom business days

Chapter 6: Handling Missing Data

Understanding missing data

Performing data quality checks

Handling missing data with univariate imputation using pandas

Handling missing data with univariate imputation using scikit-learn

Handling missing data with multivariate imputation

Handling missing data with interpolation

Chapter 7: Outlier Detection Using Statistical Methods

The nature of outliers in time-series data

Resampling time-series data

Detecting outliers using visualizations.

Detecting outliers using the Tukey method

Detecting outliers using a z-score

Detecting outliers using a modified z-score

Detecting outliers using the Hampel filter

Chapter 8: Exploratory Data Analysis and Diagnosis

Decomposing time series data

Detecting time series stationarity

Applying power transformations

Testing for autocorrelation

Chapter 9: Building Univariate Time Series Models Using Statistical Methods

Plotting ACF and PACF

Forecasting univariate time series data with exponential smoothing

Forecasting univariate time series data with non-seasonal ARIMA

Forecasting univariate time series data with seasonal ARIMA

Forecasting univariate time series with auto_arima

Chapter 10: Additional Statistical Modeling Techniques for Time Series

Forecasting univariate time series data using Prophet

Forecasting univariate time series data with Theta

Forecasting multivariate time series data using VAR

Evaluating VAR models

Forecasting volatility in financial time series data with GARCH

Automated univariate forecasting using the StatsForecast library

Chapter 11: Forecasting Using Supervised Machine Learning

Understanding supervised ML

Preparing time-series data for supervised learning

One-step forecasting with scikit-learn

Multi-step forecasting with scikit-learn

Forecasting with sktime

Hyperparameter tuning with sktime

Forecasting with exogenous variables and ensemble learning in sktime

Forecasting with XGBoost

Chapter 12: Deep Learning for Time Series Forecasting

Understanding artificial neural networks

Forecasting with LSTM (Keras, Darts, and NeuralForecast)

Handling multivariate time series and exogenous variables.

Hyperparameter tuning for deep learning forecasters

Forecasting with TCNs

Forecasting with Transformers (N-HiTS)

Chapter 13: Outlier Detection Using Unsupervised Machine Learning

Understanding considerations for time series outlier detection

Detecting outliers using distance-based algorithms

Detecting outliers using clustering-based algorithms

Detecting outliers using probabilistic and statistical algorithms

Detecting outliers using kernel-based algorithms (PyOD)

Detecting outliers using ensemble method algorithms

Detecting outliers using deep learning-based algorithms (AutoEncoder)

Chapter 14: Advanced Techniques for Complex Time Series

Understanding state-space models

Decomposing time series with multiple seasonal patterns using MSTL

Forecasting with multiple seasonal patterns using the Unobserved Components Model (UCM)

Forecasting time series with multiple seasonal patterns using AutoTBATS

Forecasting time series with multiple seasonal patterns using NeuralProphet

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ISBN:

1-80512-299-1

9781805122999

OCLC:

1564841798