Time Series Forecasting with Statistical Neural Network using Continuous Wavelet Decomposition

We propose a forecasting model based on the combined efficiency of the artificial neural network and wavelet transform in modelling time series data. The data used were decomposed into continuous wavelet signals on a scale of 10. Each of the decomposed series was subjected to correlation test with the original data. We compare the new model's performance with the conventional time series regression model (TSRM) and the wavelet neural network (WNN) forecasting model. The WNN model performed better than the TSRM. The analysis also showed that except in extremely rare cases, all the wavelet series performed optimally compared to the original data.

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