Autoregressive Integrated Moving Average (ARIMA)

Autoregressive Integrated Moving Average (ARIMA) is a statistical model used for forecasting time series data, which is data collected over time. It combines three components: "autoregressive" (predicting future values based on past values), "integrated" (analyzing the difference between values to make the series stationary), and "moving average" (using past forecast errors to improve predictions). ARIMA helps identify trends and patterns in data, making it useful for applications like economics, finance, and weather forecasting. By effectively modeling these patterns, ARIMA can provide accurate future predictions based on historical data.