An ARIMA (AutoRegressive Integrated Moving Average) model is a popular statistical method used for time series forecasting that captures various aspects of temporal dependencies in data. This model combines autoregressive components, differencing to make the data stationary, and moving average terms to effectively model and predict future values. It’s particularly useful in parametric spectral estimation as it allows for the understanding of periodic behavior and trends within time series data.
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