Fuzzy time series theory is a concept of artificial intelligence that can use to conduct forecasting technique.. This paper discusses the fuzzy logic concept to develop the base of the fuzzy time series with time invariant and time variant methods. There are several methods of fuzzy time series, including Markov Chain method and Chen and Hsu method. The Markov Chain method combines between the fuzzy time series and the Markov Chain. This merger goals to finest opportunity of the use matrix probability transitions. Chen and Hsu method is based on the historical data difference in conducting forecasting. By using Markov Chain and Chen & Hsu methods, it may achieve forecasting outcomes with a low mistakes rate. To clarify each technique and for comparison further, it is given an example of the relevant issue to be resolved by both methods. The consequences acquired can be compared, so it can be concluded which method is better.
Use of the conventional forecasting method, which is based on trend data with average sales in the last few months, results inaccurate forecasting due to a large difference in data, this is the same as fuzzy forecasting with the same interval length or static. Therefore, this paper recommends using the Cheng forecasting method combined with the Particle Swarm Optimization (PSO) algorithm. We use an artificial intelligence, i.e., PSO algorithm to set non-static length of intervals each cluster on Cheng method. The comparison of this method yields a better root mean square error (RMSE) value for each cluster on the recommended method.
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