Predicting Time SeriesUsing an Automatic New Algorithm of the Kalman Filter

Juan D. Borrero () and Jesus Mariscal
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Juan D. Borrero: Department of Business Management and Marketing, University of Huelva, E21002 Huelva, Spain
Jesus Mariscal: Department of Business Management and Marketing, University of Huelva, E21002 Huelva, Spain

Mathematics, 2022, vol. 10, issue 16, 1-13

Abstract: Time series forecasting is one of the main venues followed by researchers in all areas. For this reason, we develop a new Kalman filter approach, which we call the alternative Kalman filter. The search conditions associated with the standard deviation of the time series determined by the alternative Kalman filter were suggested as a generalization that is supposed to improve the classical Kalman filter. We studied three different time series and found that in all three cases, the alternative Kalman filter is more accurate than the classical Kalman filter. The algorithm could be generalized to time series of a different length and nature. Therefore, the developed approach can be used to predict any time series of data with large variance in the model error that causes convergence problems in the prediction.

Keywords: time series; forecasting; econometrics; Kalman filter; state-space systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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