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

Winita Sulandari; S Subanar; Muhammad Hisyam Lee; Paulo Canas Rodrigues

doi:10.1016/j.mex.2020.101015

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

MethodsX. 2020 Jul 29:7:101015. doi: 10.1016/j.mex.2020.101015. eCollection 2020.

Authors

Winita Sulandari¹, S Subanar², Muhammad Hisyam Lee³, Paulo Canas Rodrigues⁴

Affiliations

¹ Study Program of Statistics, Universitas Sebelas Maret, Indonesia.
² Departement of Mathematics, Universitas Gadjah Mada, Indonesia.
³ Department of Mathematical Sciences, Universiti Teknologi Malaysia, Malaysia.
⁴ Department of Statistics, Federal University of Bahia, Brazil.

Abstract

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.

Keywords: Deterministic model; Hybrid methodology; Nonlinear stochastic model; Weighted fuzzy time series.