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How to fit ARIMA models with missing values?
You can fit ARIMA models with missing values easily because all ARIMA models are state space models and the Kalman filter, which is used to fit state space models, deals with missing values exactly by simply skipping the update phase. So, “putting together the likelihood with missing data” is absolutely possible, as is done by the Kalman filter.
How to fit time series with missing values?
Values are missing for several weeks, sometimes randomly but often in chunks of 4-5 weeks. I want to fit a time series model to the data for forecasting using the “arima” function. Does the function “arima” take into account the missing weeks?
What are the results of fitting ARIMA to time series?
The results given by stats::arima in the first approach ( ar1) are correct: they have taken into account the missing values. In the second one, they have not.
What is Arima and how is it used in forecasting?
This post focuses on a particular type of forecasting method called ARIMA modeling. ARIMA, short for ‘AutoRegressive Integrated Moving Average’, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values. 2. Introduction to ARIMA Models
How to convert a TS to an ARIMA?
If your data isn’t ts, it will be converted by using as.ts, which discards the date information; this means that the explicit NA ‘s in the first approach are retained, while the implicit ones in the second will not appear at all and it will indeed just glue the series together.
Why does Arima expect an object of class TS?
The reason why stats::arima expects an object of class ts is because that class enforces regularly sampled data (at a certain frequency ), whereas xts can carry arbitrarily sampled data, and classical ARIMA models are defined for regularly sampled data only. Thanks for contributing an answer to Cross Validated!