Fuzzy time series Markov Chain and Fuzzy time series Chen & Hsu for forecasting

Abstract

Abstract 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.