Weighted Linear Recurrent Forecasting in Singular Spectrum Analysis

被引:2
|
作者
Kalantari, Mahdi [1 ]
Hassani, Hossein [2 ]
Silva, Emmanuel Sirimal [3 ]
机构
[1] Payame Noor Univ, Dept Stat, Tehran 193954697, Iran
[2] Univ Tehran, Res Inst Energy Management & Planning, Tehran, Iran
[3] Fash Univ Arts London, London Coll, Fash Business Sch, Ctr Fash Business & Innovat Res, London WC1V 7EY, England
来源
FLUCTUATION AND NOISE LETTERS | 2020年 / 19卷 / 01期
关键词
Time series; forecasting; singular spectrum analysis; recurrent forecasting; DIAGNOSIS;
D O I
10.1142/S0219477520500108
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 50 条