Backpropagation Networks for Time Series Forecasting: Case Studies in Data Modeling

A commercial software package, NeuroShell 2 , was used to apply a standard backpropagation neural network for forecasting the behavior of various types of time series, x = x(t). The series used covered a wide range of complexity: linear, quadratic, sinusoidal, damped sinusoidal, and finally a "real-world" application using sample stock data provided with the software. Depending on the case, the data was modeled using different methods of varying difficulty. For the linear and quadratic relations, reasonable results were obtained simply by mapping values of x(t) to their corresponding t. In the sinusoidal cases, values for x(t) were inputted to the network in small groups, having it predict only the next point after each group. Finally, in the stocks case, a complicated approach using a large number of relevant variables was employed. Overall, there was a direct relationship between the complexity of the series and the complexity of the model required.