SSA-based hybrid forecasting models and applications

Winita Sulandari · Subanar Subanar · Suhartono Suhartono · Herni Utami · Muhammad Hisyam Lee · Paulo Canas Rodrigues

29 citations0/0 files storedBulletin of Electrical Engineering and Informatics

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Forecasting Techniques and Applications Energy Load and Power Forecasting Time Series Analysis and Forecasting

Abstract

This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural networks. Trend and harmonic function were considered as the deterministic part identified based on SSA decomposition. Meanwhile, NN was intended to handle the nonlinearity relationship in the stochastic part. These two SSA-based hybrid models were contemplated to be more flexible than TLSAR and more applicable to the series with an intricate pattern. The experimental studies to the monthly accidental deaths in USA and daily electricity load Jawa-Bali showed that the proposed SSA-based hybrid model reduced RMSE for the testing data from that obtained by TLSAR model up to 95%.

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References

2003Time series forecasting using a hybrid ARIMA and neural network model 2002Introduction to Time Series and Forecasting 2011Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing 2001A simulation study of artificial neural networks for nonlinear time-series forecasting 2010A review on singular spectrum analysis for economic and financial time series 2007Forecasting time series with multiple seasonal patterns 1998Introduction to Time Series and Forecasting

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2020Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks

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