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object . The result of an arima fit. n.ahead . The number of steps ahead for which prediction is required. newxreg. New values of xreg to be used for prediction. Must have at least n.ahead rows. se.fit. Logical: should standard errors of prediction be returned? … arguments passed to or from other methods.
object : The result of an arima fit.. n.ahead : The number of steps ahead for which prediction is required. newxreg: New values of xreg to be used for prediction. Must have at least n.ahead rows.. se.fit: Logical: should standard errors of prediction be returned?, Calling forecast on a time series object, xreg, and arima xreg model seems to fail. The goal is: 1 – Split all data into into separate train and test data. 2 – Fit the train set to create train model. 3 -.
Details. Finite-history prediction is used, via KalmanForecast.This is only statistically efficient if the MA part of the fit is invertible, so predict.Arima will give a warning for non-invertible MA models.. The standard errors of prediction exclude the uncertainty in the estimation of.
Next, I show how to obtain forecasts in R upon an ARIMA model with the outliers detected by ‘tsoutliers’. The key is to the define properly the argument newxreg that is passed to predict. (This is only to illustrate the answer to your question about how to treat outliers when forecasting, I don’t address the issue whether the resulting model or forecasts are the best solution.), $begingroup$ Make sure you consider different days-of-week effects, different months-of-the-the year effects, level shift effects, local time trend effects , lead and lag effects around holidays/events , particular days-of-the-month effects , particular weeks-of-the-month effects , anomalous data points , long-weekend effects etc.. The data you are attempting to analyze in statistical sense …
