Autocorrelation, also known as serial correlation, is a statistical concept that refers to the degree of similarity between a given time series and a lagged version of itself over successive time intervals. It’s an essential tool in time-series analysis, used for identifying non-random patterns, understanding underlying trends, or even predicting future values.
Given its importance in fields such as economics, business analytics, weather forecasting, signal processing, and various branches of engineering, having a firm grasp on autocorrelation can be pivotal. Understanding how variables change over time and how one time period relates to another can provide valuable insights into data behavior.
