Kalantari, M. and Hassani, H. and Silva, E.S. (2019) Weighted Linear Recurrent Forecasting in Singular Spectrum Analysis. Fluctuation and Noise Letters, 19 (1). ISSN 1793-6780
Type of Research: | Article |
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Creators: | Kalantari, M. and Hassani, H. and Silva, E.S. |
Description: | Singular Spectrum Analysis (SSA) is an increasingly popular time series filtering and forecasting technique. Owing to its widespread applications in a variety of fields, there is a growing interest towards improving its forecasting capabilities. As such, this paper takes into consideration the Recurrent forecasting approach in SSA (SSA-R) and presents a new mechanism for improving the accuracy of forecasts attainable via this method. The proposed Recurrent SSA-R approach is referred to as Weighted SSA-R (W:SSA-R), and we propose using a weighting algorithm for weigthing the coefficients of the Linear Recurrent Relation (LRR). The performance of forecasts from the W:SSA-R approach are compared with forecasts from the established SSA-R approach. We exploit real data and various simulated time series for the comparison, so as to provide the reader with more conclusive findings. Our results confirm that the W:SSA-R approach can provide comparatively more accurate forecasts and is indeed a viable solution for improving forecasts by SSA. |
Official Website: | https://www.worldscientific.com/woldscinet/fnl |
Keywords/subjects not otherwise listed: | Time series, forecasting, singular spectrum analysis, recurrent forecasting |
Your affiliations with UAL: | Colleges > London College of Fashion |
Date: | 7 August 2019 |
Digital Object Identifier: | doi.org/10.1142/S0219477520500108 |
Date Deposited: | 29 Apr 2020 12:52 |
Last Modified: | 21 Aug 2020 12:46 |
Item ID: | 15623 |
URI: | https://ualresearchonline.arts.ac.uk/id/eprint/15623 |
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