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)

Writing time series data to InfluxDB

When working with large time series data, such as a sensor or Internet of Things (IoT) data, you will need a more efficient way to store and query such data for further analytics. This is where time series databases shine, as they are built exclusively to work with complex and very large time series datasets.

In this recipe, we will work with InfluxDB as an example of how to write to a time series database.

Getting ready

You will be using the ExtraSensory dataset, a mobile sensory dataset made available by the University of California, San Diego, which you can download here: http://extrasensory.ucsd.edu/.

There are 278 columns in the dataset. You will be using two of these columns to demonstrate how to write to InfluxDB. You will be using the timestamp (date ranges from 2015-07-23 to 2016-06-02, covering 152 days) and the watch accelerometer reading (measured in milli G-forces or milli-G).

Before you can interact with InfluxDB using...