Research Article Open Access

Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model

Suhartono1, B. S.S. Ulama2 and A. J. Endharta2
  • 1 , Afganistan
  • 2 ,
American Journal of Applied Sciences
Volume 7 No. 10, 2010, 1372-1378

DOI: https://doi.org/10.3844/ajassp.2010.1372.1378

Submitted On: 3 January 2010 Published On: 31 October 2010

How to Cite: Suhartono, Ulama, B. S. & Endharta, A. J. (2010). Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model. American Journal of Applied Sciences, 7(10), 1372-1378. https://doi.org/10.3844/ajassp.2010.1372.1378

Abstract

Problem statement: The aim of this research was to study further some latest progress of wavelet transform for time series forecasting, particularly about Neural Networks Multiscale Autoregressive (NN-MAR). Approach: There were three main issues that be considered further in this research. The first was some properties of scale and wavelet coefficients from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition, particularly at seasonal time series data. The second focused on the development of model building procedures of NN-MAR based on the properties of scale and wavelet coefficients. Then, the third was empirical study about the implementation of the proposed procedure and comparison study about the forecast accuracy of NN-MAR to other forecasting models. Results: The results showed that MODWT at seasonal time series data also has seasonal pattern for scale coefficient, whereas the wavelet coefficients are stationer. The result of model building procedure development yielded a new proposed procedure of NN-MAR model for seasonal time series forecasting. In general, this procedure accommodated input lags of scale and wavelet coefficients and other additional seasonal lags. In addition, the result showed that the proposed procedure works well for determining the best NN-MAR model for seasonal time series forecasting. Conclusion: The comparison study of forecast accuracy showed that the NN-MAR model yields better forecast than MAR and ARIMA models.

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Keywords

  • Neural networks
  • multiscale
  • MODWT
  • NN-MAR
  • seasonal
  • time series