Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis

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 be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples.

You'll start 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.

Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in 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 practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is a prerequisite. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.

Table of Contents
Getting Started with Time Series Analysis
Reading Time Series Data from Files
Reading Time Series Data from Databases
Persisting Time Series Data to Files
Persisting Time Series Data to Databases
Working with Date and Time in Python
Handling Missing Data
Outlier Detection Using Statistical Methods
Exploratory Data Analysis & Diagnosis
Building Univariate Models using Statistical Methods
Advanced Statistical Modeling Techniques for Time Series
Forecasting Using Supervised Machine Learning
Deep Learning for Time Series Forecasting
Outlier Detection Using Unsupervised Machine Learning
Working with Multiple Seasonality in Time Series
(N.B. Please use the Read Sample option to see further chapters)