Feature generation for time series

What is a feature rollout? A feature rollout is the software development process of introducing a new feature to a set of users . In the not so recent past, software was rolled out once every week or two, with a number of changes being bundled together, and then monitored.

Feature engineering is the process that takes raw data and transforms it into features that can be used to create a predictive model using machine learning or statistical modeling, such as deep learning .

A time series is a collection of observations of well-defined data items obtained through repeated measurements over time . For example, measuring the value of retail sales each month of the year would comprise a time series.

Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time . In time series analysis, analysts record data points at consistent intervals over a set period of time rather than just recording the data points intermittently or randomly.

Time-series data have core components like seasonality, trend, and cycles . For example, ice-cream sales usually have yearly seasonality — you can reasonably predict the next summer’s sales based on this year’s. Similarly, temperatures or air quality measurements have daily seasonality or also, yearly.21 Tem 2021

Feature extraction is the practice of enhancing machine learning by finding characteristics in the data that help solve a particular problem. For time series data, feature extraction can be performed using various time series analysis and decomposition techniques.