A 3D neural network for business forecasting

Describes a neural network approach for time series forecasting. This approach has several significant advantages over other conventional forecasting methods such as regression and Box-Jenkins. Besides simplicity, another major advantage is that it does not require any assumption to be made about the underlying function or model to be used. All it needs are the historical data of the target and those relevant input factors for training the network. In some cases, even the historical targets alone are sufficient to train the network for forecasting. Once the network is well trained and the error between the target and the network forecasts has converged to an acceptable level, it is ready for use. The proposed network has a 3-dimensional structure which is proposed for capturing the temporal information contained in the input time series. Several real applications, including forecasting of electricity load, stock market and interbank interest rate forecastings were tested with the proposed network and the findings were very encouraging