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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics
Muhammad Hisyam Lee
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Artificial neural network forecasting performance with missing value imputations Nur Haizum Abd Rahman; Muhammad Hisyam Lee
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.501 KB) | DOI: 10.11591/ijai.v9.i1.pp33-39

Abstract

This paper presents time series forecasting method in order to achieve high accuracy performance. In this study, the modern time series approach with the presence of missing values problem is developed. The artificial neural networks (ANNs) is used to forecast the future values with the missing value imputations methods used known as average, normal ratio and also the modified method. The results are validated by using mean absolute error (MAE) and root mean square error (RMSE). The result shown that by considering the right method in missing values problems can improved artificial neural network forecast accuracy. It is proven in both MAE and RMSE measurements as forecast improved from 8.75 to 4.56 and from 10.57 to 5.85 respectively. Thus, this study suggests by understanding the problem in time series data can produce accurate forecast and the correct decision making can be produced.
SSA-based hybrid forecasting models and applications Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami; Muhammad Hisyam Lee; Paulo Canas Rodrigues
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.132 KB) | DOI: 10.11591/eei.v9i5.1950

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%.
Hybrid SSA-TSR-ARIMA for water demand forecasting Suhartono Suhartono; Salafiyah Isnawati; Novi Ajeng Salehah; Dedy Dwi Prastyo; Heri Kuswanto; Muhammad Hisyam Lee
International Journal of Advances in Intelligent Informatics Vol 4, No 3 (2018): November 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i3.275

Abstract

Water supply management effectively becomes challenging due to the human population and their needs have been growing rapidly. The aim of this research is to propose hybrid methods based on Singular Spectrum Analysis (SSA) decomposition, Time Series Regression (TSR), and Automatic Autoregressive Integrated Moving Average (ARIMA), known as hybrid SSA-TSR-ARIMA, for water demand forecasting. Monthly water demand data frequently contain trend and seasonal patterns. In this research, two groups of different hybrid methods were developed and proposed, i.e. hybrid methods for individual SSA components and for aggregate SSA components. TSR was used for modeling aggregate trend component and Automatic ARIMA for modeling aggregate seasonal and noise components separately. Firstly, simulation study was conducted for evaluating the performance of the proposed methods. Then, the best hybrid method was applied to real data sample. The simulation showed that hybrid SSA-TSR-ARIMA for aggregate components yielded more accurate forecast than other hybrid methods. Moreover, the comparison of forecast accuracy in real data also showed that hybrid SSA-TSR-ARIMA for aggregate components could improve the forecast accuracy of ARIMA model and yielded better forecast than other hybrid methods. In general, it could be concluded that the hybrid model tends to give more accurate forecast than the individual methods. Thus, this research in line with the third result of the M3 competition that stated the accuracy of hybrid method outperformed, on average, the individual methods being combined and did very well in comparison to other methods.
Co-Authors Dedy Dwi Prastyo Heri Kuswanto Herni Utami Novi Ajeng Salehah Nur Haizum Abd Rahman Paulo Canas Rodrigues Salafiyah Isnawati Subanar . Suhartono Suhartono Suhartono Suhartono Winita Sulandari