A primer on neural networks for forecasting

Article Abstract:

Artificial neural network models, which are flexible nonlinear models, are gaining acceptance in the electric utility industry for short-term forecasting. With the neural network's framework, a flexible function that can estimate a variety of nonlinear processes is achieved. Neural networks also offer substantial advantages in forecasting problems where nonlinearities and variable interactions play a vital role. However, confusion and controversy plague the topic of neural networks, which partly reveal that a different language is used for neural networks compared to what is used in the more popular area of econometrics.

Electronic computers, Electronic Computer Manufacturing, Artificial Intelligence Systems, Electric utilities, Forecasts and trends, Interview, Artificial intelligence, Nonlinear programming

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A neural network approach to forecasting financial distress

Article Abstract:

The use of neural network (NN) computing systems is said to be more reliable than the multiple discriminant analysis (MDA) approach in determining the financial health of firms. A study was conducted to confirm this assertion. Two sets of studies were made, with each set having 47 healthy and 47 financially distressed firms as subjects. The first set used only NN while the second one used both NN and MDA. Findings revealed that NN is truly more accurate than MDA in determining financial standing of firms.

Software, Finance, Financial software, Business enterprises

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Neural networks vs. conventional methods of forecasting

Article Abstract:

Neural network forecasting can outperform most types of conventional forecasting models. In neural networks, relevant examples are programmed and assimilated to develop underlying relationships that can be learned by a network. Relationships do not have to be specified in advance in such networks, and they also do not require assumptions regarding underlying population distributions.

Methods

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