autoregressive integrated moving average (arima) modeling
Autoregressive integrated moving average (ARIMA) modeling is a statistical technique used to forecast future values based on past observations. It combines autoregression (AR) to capture the relationship between current and previous observations, differencing (I) to make the time series stationary, and moving average (MA) to incorporate the influence of past forecast errors. ARIMA models are widely used in analyzing and predicting time series data, such as financial markets, economic indicators, and climate patterns.
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