FORECASTING INDUSTRIAL PRODUCTION INDEX WITH SOFT COMPUTING TECHNIQUES

Abstract

The production industry has a dynamic structure that is affected by socioeconomic factors such as economical policies, stabilization and competition increasing with globalization and also incorporates complex manufacturing processes. One of the most important indicators that demonstrates the current situation and development of manufacturing industry in time is manufacturing index. Effective planning for manufacturing industry depends on accurate and realistic predictions for future of sector. Soft computing techniques such as artificial neural networks (ANN) and fuzzy inference systems (FIS) draw attention along with classical time series in prediction applications recently. In this study, monthly production index is forecasted by using adaptive neuro- fuzzy inference systems (ANFIS) and two different learning of algorithms of ANN models (multi layer perceptron -MLP and radial basis function network-RBFN). This index was also predicted by SARIMA model which is one of the classical time series analysis method and prediction performances of classical method and soft computing methods were then compared.