Forecasting by neural networks in the wavelet domain

This paper presents a forecasting method for time series. This method combines the wavelet analysis and several forecasting techniques such as Artificial Neural Networks (ANN), linear regression and random walk. The proposed method is tested using three real time series: the first contains historical data recorded during eight weeks from a WiMAX network and the other two are based on financial series. It is shown that AI with wavelet analysis can be an efficient and versatile approach in time series prediction for small periods time interval (up to 1 month). For long time interval, the best method used is Linear Regression technique. Also we compared the results obtained using various types of wavelets. The results show that Daubechies 1 (db1) and Reverse biorthogonal 1 (rbio1.1) give the best results.

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