ARIMA (Auto-Regressive Integrated Moving Average)

ARIMA (Auto-Regressive Integrated Moving Average) is a statistical method used to analyze and forecast time series data—information collected over time, like sales or temperatures. It combines three components: autoregression (using past data points to predict future ones), differencing (transforming data to make it more stable and stationary), and moving average (using past forecast errors to improve predictions). By capturing patterns and trends within the data, ARIMA helps generate accurate forecasts, making it a powerful tool for understanding and predicting future values based on historical observations.