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The ARIMA model is widely used for time series forecasting because it combines two key components: autoregressive (AR), which uses past data points to model future values, and moving average (MA), which smooths out short-term fluctuations in the data. Additionally, integration (I) is used to make a non-stationary time series stationary by differencing the data. This allows ARIMA to be applied to a wide range of time series data, even if they exhibit complex patterns, provided the data can be made stationary. Option A is incorrect because ARIMA requires the data to be stationary (or at least made stationary through differencing). Option B is incorrect because ARIMA can handle data with both long-term trends and periodic fluctuations. Option D is incorrect because ARIMA is not the best model for time series with seasonal components—SARIMA (Seasonal ARIMA) is more appropriate for that. Option E is incorrect because ARIMA can handle irregular components as long as the data is stationary or can be made stationary.
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