Employing genetic algorithm to optimize OWA-fuzzy forecasting model

Accuracy of forecasting in fuzzy based prediction system considerably depends on subjectively decided parameters such as fuzzy membership function. In this paper, we presented a novice concept to optimize Ordered Weight Aggregation (OWA) based forecasting model by Genetic Algorithm. Firstly, OWA weights are determined on the basis of importance of fuzzy set in the system by employing regularly increasing monotonic (RIM) quantifiers. Subsequently, genetic algorithm is employed to generate wide range of parameters for fuzzy membership functions (mf) in the region of time series. Lastly, forecasted value is obtained by OWA aggregation of past fuzzy observations generated at prior time (t, t-1, t-2). Proposed optimized forecasting model has been compared with some pre-existing models on same data. Results demonstrate that forecasting performance of the proposed model has greatly improved by reducing mean square error (MSE) and mean absolute percentage error (MAPE).