ARIMA Models

ARIMA (AutoRegressive Integrated Moving Average) models are statistical tools used to analyze and forecast time series data, like sales or weather patterns. They identify patterns based on past observations, including trends and cycles, by combining three components: autoregression (using past values), differencing (to make data more stable), and moving averages (based on past errors). By modeling these elements, ARIMA can predict future points in the series, helping businesses and researchers make data-driven decisions. Essentially, it captures the underlying structure of data to anticipate future behavior accurately.