Book Image

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
Book Image

Time Series Analysis with Python Cookbook

By: Tarek A. Atwan

Overview of this book

Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch. Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.
Table of Contents (18 chapters)

Installing Python libraries

In the preceding recipe, you were introduced to the YAML environment configuration file, which allows you to create a Python virtual environment and all the necessary packages in one step using one line of code:

$ conda env create -f environment.yml

Throughout this book, you will need to install several Python libraries to follow the recipes. There are several methods for installing Python libraries, which you will explore in this recipe.

Getting ready

You will create and use different files in this recipe, including a requirements.txt, environment_history.yml, and other files. These files are available to download from the GitHub repository for this book: https://github.com/PacktPublishing/Time-Series-Analysis-with-Python-Cookbook./tree/main/code/Ch1.

In this chapter, you will become familiar with how to generate your requirements.txt file, as well as installing libraries in general.

How to do it…

The easiest way to install...