Prediction of time series by the method of analogs

Prediction of time series by the method of analogs We consider an algorithm of prediction of nonstationary time series based on the method of analogs. Since the exhaustion of a great number of versions is required for the adjustment of the parameters of the optimal prognostic model, we describe a genetic algorithm used in this case. We consider several procedures of construction of prognostic models. The numerical results are used to choose the procedure guaranteeing the minimum mean square error. The parameters of the model affecting the quality of predictions are determined. The proposed method is tested by using the reanalysis data (NCEP/NCAR project) on the anomalies of the monthly average surface air temperature for 58 yr. The results of predictions are compared with the estimates obtained by the linear regression method. It is shown that the method of analogs gives satisfactory results even in the cases where the regression methods lead to errors equal to the variance of predicted series. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Oceanography Springer Journals

Prediction of time series by the method of analogs

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Publisher
Springer US
Copyright
Copyright © 2007 by Springer Science+Business Media, Inc.
Subject
Geosciences; Oceanography; Remote Sensing/Photogrammetry; Meteorology/Climatology; Climate Change; Environmental Physics
ISSN
0928-5105
eISSN
0928-5105
D.O.I.
10.1007/s11110-007-0019-3
Publisher site
See Article on Publisher Site

Abstract

We consider an algorithm of prediction of nonstationary time series based on the method of analogs. Since the exhaustion of a great number of versions is required for the adjustment of the parameters of the optimal prognostic model, we describe a genetic algorithm used in this case. We consider several procedures of construction of prognostic models. The numerical results are used to choose the procedure guaranteeing the minimum mean square error. The parameters of the model affecting the quality of predictions are determined. The proposed method is tested by using the reanalysis data (NCEP/NCAR project) on the anomalies of the monthly average surface air temperature for 58 yr. The results of predictions are compared with the estimates obtained by the linear regression method. It is shown that the method of analogs gives satisfactory results even in the cases where the regression methods lead to errors equal to the variance of predicted series.

Journal

Physical OceanographySpringer Journals

Published: Dec 29, 2007

References

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