Python Machine Learning Cookbook

Chapter 8. Dissecting Time Series and Sequential Data

In this chapter, we will cover the following recipes:

• Transforming data into the time series format
• Slicing time series data
• Operating on time series data
• Extracting statistics from time series data
• Building Hidden Markov Models for sequential data
• Building Conditional Random Fields for sequential text data
• Analyzing stock market data using Hidden Markov Models

Introduction

Time series data is basically a sequence of measurements that are collected over time. These measurements are taken with respect to a predetermined variable and at regular time intervals. One of the main characteristics of time series data is that the ordering matters!

The list of observations that we collect is ordered on a timeline, ...