How effective are neural networks at forecasting and prediction? A review and evaluation

Article Abstract:

An evaluation of 48 studies on the application of artificial neural networks to business forecasting and prediction was conducted to determine the effectiveness of their validation and implementation. Results showed that 11 of the studies were both effectively validated and implemented, while another 11 were effectively validated and yielded favorable results, but some problems were detected with their implementation. Eighteen of these 22 studies affirm the potential of artificial neural networks for forecasting and prediction.

Forecasting, Models, Computer networks, Evaluation, Neural networks, Business forecasting

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Causal forces: structuring knowledge for time-series extrapolation

Article Abstract:

A technique for structuring domain knowledge for use in quantitative extrapolation represents causal forces as inputs, since this can result in greater savings, accuracy and judgment integration than standard extrapolation methods. This technique is unique because it does not incorporate the common but unwise assumption that causal forces support trends. Instead, an inserted selection rule forbids trends from being extrapolated when they are contrary tocausal force directions.

Usage, Forecasts and trends, Time-series analysis, Time series analysis, Rule-based systems, Rule based systems, Causality (Physics)

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Correspondence on the selection of error measures for comparisons among forecasting methods

Article Abstract:

A study criticizing the selection of error measures for comparisons among forecasting methods was conducted. The Generalized Forecast Error Second Moment is not an improvement over the Mean Square Error in forecasting performance because forecast accuracy may be determined by other criteria. Also, simulated data used in the proof of the method were not a good representation of actual data.

Methods, Forecasting, Error analysis (Mathematics)

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