Understanding sequential data
In the world of machine learning, we encounter many types of data, such as images, text, video, sensor readings, and so on. Different types of data require different types of modeling techniques. Sequential data refers to data where the ordering is important. Time-series data is a particular manifestation of sequential data. It is basically time-stamped values obtained from any data source such as sensors, microphones, stock markets, and so on. Time-series data has a lot of important characteristics that need to be modeled in order to effectively analyze the data.
The measurements that we encounter in time-series data are taken at regular time intervals and correspond to predetermined parameters. These measurements are arranged on a timeline for storage, and the order of their appearance is very important. We use this order to extract patterns from the data.
In this chapter, we will see how to build models that describe the given time-series data or any sequence...
