An intelligent business forecaster for strategic business planning

Article Abstract:

A form of business planning strategy attempted to use an intelligent business forecasting (IBF) software, a multi-layered fuzzy rule-based neural network which incorporated the basic elements and functions of a typical fuzzy logic inference into a neural network architecture. The establishment of the IBF architecture involved four stages: self-organized learning, fuzzy rule identification, supervised learning and forecasting and retraining. This type of business forecaster showed superior results compared to other neural networks in terms of both learning speed and accuracy.

Fuzzy logic, Business planning, Strategic planning (Business), Fuzzy systems

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Time series forecasting using neural networks: should the data be deseasonalized first?

Article Abstract:

A study has been conducted to investigate whether prior statistical deseasonalization of data is necessary to produce more accurate neural; network forecasts. Time series forecasts were compared, based on neural networks, with forecasts from six traditional time series methods. Findings have revealed that neural networks may benefit from deseasonalizing data just as statistical methods do. Since neural networks that do not use deseasonalized time series may have to learn trends, cyclical components and seasonality.

Financial Forecasting, Economic forecasting, Seasonal variations (Economics)

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An intelligent model selection and forecasting system

Article Abstract:

An empirical research on model selection and forecasting utilized an intelligent decision-support system based on the neural network technology. The intelligent system was developed based on time-series characteristics. The neural network technique generated a framework for directly involving time-series characteristics into the model-selection process. The forecasting system accomplished a reasonable level of accuracy in the testing phase of the research.

Mathematical models, Forecasting

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