TY  - JOUR
AU  - Suhartono, 
AU  - Ulama, B. S.S. 
AU  - Endharta, A. J. 
PY  - 2010
TI  - Seasonal Time Series Data Forecasting by Using Neural Networks Multiscale Autoregressive Model
JF  - American Journal of Applied Sciences
VL  - 7
IS  - 10
DO  - 10.3844/ajassp.2010.1372.1378
UR  - https://thescipub.com/abstract/ajassp.2010.1372.1378
AB  - 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.