Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks

被引:22
|
作者
Sulandari, Winita [1 ]
Subanar, S. [2 ]
Lee, Muhammad Hisyam [3 ]
Rodrigues, Paulo Canas [4 ]
机构
[1] Univ Sebelas Maret, Study Program Stat, Surakarta, Indonesia
[2] Univ Gadjah Mada, Dept Math, Yogyakarta, Indonesia
[3] Univ Teknol Malaysia, Dept Math Sci, Johor Baharu, Malaysia
[4] Univ Fed Bahia, Dept Stat, Salvador, BA, Brazil
关键词
Hybrid methodology; Deterministic model; Nonlinear stochastic model; Weighted fuzzy time series; ENROLLMENTS; MODEL; DEMAND;
D O I
10.1016/j.mex.2020.101015
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Hybrid methodologies have become popular in many fields of research as they allow researchers to explore various methods, understand their strengths and weaknesses and combine them into new frameworks. Thus, the combination of different methods into a hybrid methodology allows to overcome the shortcomings of each singular method. This paper presents the methodology for two hybrid methods that can be used for time series forecasting. The first combines singular spectrum analysis with linear recurrent formula (SSA-LRF) and neural networks (NN), while the second combines the SSA-LRF and weighted fuzzy time series (WFTS). Some of the highlights of these proposed methodologies are: The two hybrid methods proposed here are applicable to load data series and other time series data. The two hybrid methods handle the deterministic and the nonlinear stochastic pattern in the data. The two hybrid methods show a significant improvement to the single methods used separately and to other hybrid methods. (C) 2020 The Author(s). Published by Elsevier B.V.
引用
收藏
页数:12
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