What statistical technique is commonly used for time series analysis?

Forecasting is a statistical technique that is particularly well-suited for time series analysis. Time series data consists of observations collected or recorded at specific time intervals, and the primary objective of analyzing such data often involves making predictions about future trends based on historical patterns.

In time series analysis, forecasting techniques leverage past data to identify trends, seasonality, and cycles, allowing analysts to project future values. Methods like ARIMA (AutoRegressive Integrated Moving Average) and exponential smoothing are commonly utilized within the forecasting domain to handle different characteristics of time series data, such as non-stationarity and seasonal fluctuations.

While clustering, regression, and correlation can be valuable in various analytical contexts, they do not specifically address the unique aspects of time-ordered data like forecasting does. Clustering is focused on grouping similar data points, regression is used for modeling relationships between variables, and correlation assesses the degree of association between two variables. None of these techniques are designed specifically to predict future values based on the history of a single variable over time, which is the core purpose of forecasting in time series analysis.