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JSIR Vol.71 [2012] >
JSIR Vol.71(10) [October 2012] >
| Title: | Forecasting strong seasonal time series with artificial neural networks |
| Authors: | Adhikari, Ratnadip
Agrawal, R. K. |
| Keywords: | Time series forecasting
Seasonal time series
Artificial neural networks
Holt-Winters
Box-Jenkins model
Support vector machine |
| Issue Date: | Oct-2012 |
| Publisher: | NISCAIR-CSIR, India |
| Abstract: | Many practical time series often exhibit trends and
seasonal patterns. The traditional statistical models eliminate the effect of
seasonality from a time series before making future forecasts. As a result, the
computational complexities are increased together with substantial reductions
in overall forecasting accuracies. This paper comprehensively explores the
outstanding ability of Artificial Neural Networks (ANNs) in recognizing and
forecasting strong seasonal patterns without removing them from the raw data.
Six real-world time series having dominant seasonal fluctuations are used in
our work. The performances of the fitted ANN for each of these time series are
compared with those of three traditional models both manually as well as
through a non-parametric
statistical test. The empirical results show that the
properly designed ANNs are remarkably efficient in directly forecasting strong
seasonal variations as well as outperform each of the three statistical models
for all six time series. A robust algorithm together with important practical
guidelines is also suggested for ANN forecasting of strong seasonal data. |
| Page(s): | 657-666 |
| CC License: |  CC Attribution-Noncommercial-No Derivative Works 2.5 India |
| ISSN: | 0975-1084 (Online); 0022-4456 (Print) |
| Source: | JSIR Vol.71(10) [October 2012]
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