A Comparison of The Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan
Arnita Arnita, N Afnisah, Faridawaty Marpaung
Abstract
Abstract 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.