Time Series Analysis with Python Cookbook

Get well-versed with different time series analysis and forecasting techniques using Python

Key Features

• Learn how to work with time series data for stock analysis, predictive maintenance, and anomaly detection
• Implement time series analysis techniques in Python such as ARMA, ARIMA, Auto ARIMA, SARIMA, and more
• Explore and interact with time series data through advanced visualizations in Python

Book Description

Time series data can be found almost everywhere. Anytime we work with variables that change over time, we are dealing with time series data. Such data usually contains a lot of noise, which makes it crucial to become well-versed with time series techniques for proper data preparation, analysis, and forecasting.

This book covers implementation techniques relating to time series analysis and forecasting. You'll start by setting up a virtual Python environment to install all the necessary libraries needed. The book will show you how you can read time series data from different sources and take you through various considerations when working with time series data. You'll explore different ways to store data in several formats and databases. Next, you'll work with date and time data types and then understand techniques to investigate the quality of your data when working with time series data. You'll also work through data visualization techniques for exploratory data analysis and build models using statistical methods. Later, you'll apply machine learning and deep learning techniques to your time series data. Finally, the book covers some advanced time series analysis techniques using popular Python libraries and platforms.

By the end of this time series Python book, you'll have become comfortable working with time series data in Python.

What you will learn

• Read time series data from a variety of data sources
• Write time series data for storage in all the popular databases
• Perform data imputation techniques for time series data using pandas
• Build time series visualizations using pandas, statsmodel and PyViz
• Use statistical methods for time series analysis and forecasting
• Apply classification and regression techniques to time series data
• Leverage time series databases and their built-in capabilities using Python

Who This Book Is For

This book is for data analysts, business analysts, and data scientists who want to implement time series analysis and forecasting techniques using Python. Prior knowledge of the Python programming language is assumed.

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 of Time Series Data
9. Exploratory Data Analysis and Diagnosis
10. Building Univariate Models Using Statistical Methods
11. Advanced Statistical Time Series Modeling
12. Using Machine Learning with Time Series Data
13. Deep Learning for Time Series
14. Advanced Time Series Topics
15. Working with Time Series Databases