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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
4.8 (11)
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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

4.8 (11)
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)
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Chapter 6: Working with Date and Time in Python

At the core of time-series data is time. Time-series data is a sequence of observations or data points captured in successive order. In the context of a DataFrame, time-series data has an ordered index type DatetimeIndex as you have seen in earlier chapters.

Being familiar with manipulating date and time in time-series data is an essential component of time series analysis and modeling. In this chapter, you will find recipes for common scenarios when working with date and time in time-series data.

Python has several built-in modules for working with date and time, such as the datetime, time, calendar, and zoneinfo modules. Additionally, there are other popular libraries in Python that further extend the capability to work with and manipulate date and time, such as dateutil, pytz, and arrow, to name a few.

You will be introduced to the datetime module in this chapter but then transition to use pandas for enhanced and more complex...

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