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)

Working with time deltas

When working with time-series data, you may need to perform some calculations on your datetime columns, such as adding or subtracting. Examples can include adding 30 days to purchase datetime to determine when the return policy expires for a product or when a warranty ends. For example, the Timedelta class makes it possible to derive new datetime objects by adding or subtracting at different ranges or increments, such as seconds, daily, and weekly. This includes time zone-aware calculations.

In this recipe, you will explore two practical approaches in pandas to capture date/time differences – the pandas.Timedelta class and the pandas.to_timedelta function.

How to do it…

In this recipe, you will work with hypothetical sales data for a retail store. You will generate the sales DataFrame, which will contain items purchased from the store and the purchase date. You will then explore different scenarios using the Timedelta class and the to_timedelta...