ARIMA models, or AutoRegressive Integrated Moving Average models, are a class of statistical techniques used for analyzing and forecasting time series data. They combine autoregression, differencing to achieve stationarity, and moving averages to capture various aspects of the underlying data patterns, such as trends and seasonality. Understanding ARIMA models is essential for identifying and modeling the components of time series data effectively.
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