A Comparison of The Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan

Medan has a high rainfall variability. The amount of rainfall affects the welfare of
life such as in the fields of health, economy, agriculture, industry, transportation, tourism and
so on. To find out changes in rainfall patterns, a prediction of rainfall levels is designed to see
and analyze the rainfall patterns that will form in the future. Forecasting is the art and science
of predicting future events by taking historical data and projecting it into the future by using
some form of mathematical model. One of the methods used to predict an event is the fuzzy
time series method. Fuzzy time series is a concept that can be used to predict problems where
historical data is formed in linguistic values. While the latest data as a result are in the form of
real numbers. The purpose of this research is to implement the fuzzy time series method to
predict rainfall in Medan by comparing several developments of the fuzzy time series method,
namely Fuzzy Time Series Chen, Markov Chain and Cheng. In determining the interval in the
Fuzzy time series Avergae Based rules are used to get the best results. In this study the result is
MAPE value of each method. Chen’s method give MAPE = 8.002%, Markov chain’s method
give MAPE = 30.12% and cheng’s method give MAPE =34.5 %. So the best method for
forecasting rainfall is Chen Method.