ARIMA

ARIMA (AutoRegressive Integrated Moving Average) is a statistical method used to analyze and forecast time series data—sequences of data points collected over time. It combines three components: autoregression (using past values to predict future ones), differencing (transforming data to make it stable), and moving averages (using past errors to improve predictions). By understanding and modeling these patterns, ARIMA helps identify trends and make informed forecasts about future data points, making it valuable in fields like finance, economics, and weather forecasting.