ARIMA (AutoRegressive Integrated Moving Average)

ARIMA, or AutoRegressive Integrated Moving Average, is a statistical method used for forecasting time series data. It combines three components: autoregression (using past values to predict future ones), differencing (to make the data stationary by removing trends), and moving averages (smoothing out short-term fluctuations). Essentially, ARIMA analyzes historical data patterns to predict future values, making it useful in various fields like finance, economics, and weather forecasting. By understanding the patterns in past data, it helps to identify trends and make informed predictions about what might happen next.