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)

Serializing time series data with pickle

Often when working with data in Python, you may want to persist Python data structures or objects, such as a pandas DataFrame, to disk as opposed to keeping it in memory. One technique is to serialize your data into a byte stream to store to a file. In Python, the pickle module is a popular approach to object serialization and de-serialization (the reverse of serialization), also known as pickling and unpickling, respectively.

Getting ready

The pickle module comes with Python and no additional installation is needed.

In this recipe, we will use two different methods for serializing the data, commonly referred to as pickling.

You will be using the COVID-19 dataset provided by the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, which you can download from the official GitHub repository here: https://github.com/CSSEGISandData/COVID-19.

How to do it…

You will...