Time Series Analysis with Python Cookbook

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 of the print or Kindle book includes a free PDF eBook

Key Features

• Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
• Learn different techniques for evaluating, diagnosing, and optimizing your models
• Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities

Book Description

To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.

You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.

Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.

What you will learn

• Understand what makes time series data different from other data
• Apply imputation and interpolation strategies to handle missing data
• Implement an array of models for univariate and multivariate time series
• Plot interactive time series visualizations using hvPlot
• Explore state-space models and the unobserved components model (UCM)
• Detect anomalies using statistical and machine learning methods
• Forecast complex time series with multiple seasonal patterns
• Use conformal prediction for constructing prediction intervals for time series

Who this book is for

This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want to learn time series analysis and forecasting techniques step by step through practical Python recipes.

To get the most out of this book, you’ll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help you to better utilize and apply the recipes more quickly.

Table of Contents

1. Getting Started with Time Series Analysis
2. Reading Time Series Data from Files
3. Reading Time Series Data from Databases
4. Persisting Time Series Data to Files
5. Persisting Time Series Data to Databases
6. Working with Date and Time in Python
7. Handling Missing Data
8. Outlier Detection Using Statistical Methods
9. Exploratory Data Analysis and Diagnosis
10. Building Univariate Models Using Statistical Methods

(N.B. Please use the Read Sample option to see further chapters)

Tarek A. Atwan is a data analytics expert with over 16 years of international consulting experience, providing subject matter expertise in data science, machine learning operations, data engineering, and business intelligence. He has taught multiple hands-on coding boot camps, courses, and workshops on various topics, including data science, data visualization, Python programming, time series forecasting, and blockchain at various universities in the United States. He is regarded as a data science mentor and advisor, working with executive leaders in numerous industries to solve complex problems using a data-driven approach.