RT Journal Article
T1 Predicting Time Series Using an Automatic New Algorithm of the Kalman Filter
A1 Borrero Sánchez, Juan Diego
A1 Mariscal, Jesús
AB 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 thealternative 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.
PB MDPI
SN 2227-7390 (electrónico)
YR 2022
FD 2022-08
LK https://hdl.handle.net/10272/22034
UL https://hdl.handle.net/10272/22034
LA eng
NO Borrero, J. D., & Mariscal, J. (2022). Predicting Time Series Using an Automatic New Algorithm of the Kalman Filter. In Mathematics (Vol. 10, Issue 16, p. 2915). MDPI AG. https://doi.org/10.3390/math10162915
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026