A Two Phase Algorithm for Fuzzy Time Series Forecasting using Genetic Algorithm and Particle Swarm Optimization Techniques

Abstract

Fuzzy Time series is being used for forecasting since last two decades for forecasting. Nature inspired computing techniques like other domains are now being used for optimization purpose in Fuzzy Time Series forecasting models to get improved results. In this paper we have presented a new algorithm for multivariate fuzzy time series forecasting having two phases. Genetic Algorithm and Particle Swarm Optimization techniques are used in this algorithm for optimization. We applied our algorithm on Taiwan forex Exchange (TAIFEX) index and got better results and minimized error rate as compared to previous methods.

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