Comparison of Forecasting Using Fuzzy Time Series Chen Model and Lee Model to Closing Price of Composite Stock Price Index

Abstract

Investment is an activity to invest with the hope that someday you will get a number of benefits from the
investment result. In investing, analyzing is important to see the current situation and condition of stock. Investors
can forecast stock prices by looking at trends based on data movements from stock prices in the past. Fuzzy Time
Series (FTS) was used in this study to forecast. Fuzzy time series is a forecasting technique that uses patterns from
past data to project future data in areas where linguistic values are formed in the data. This study compares the
closing price of composite stock forecasting using the fuzzy time series chen and lee models. The JCI's closing price
for the following period is 6,904 and has a Mean Absolute Percentage Error (MAPE) of 4.03%, according to the chen
fuzzy time series method. In contrast, utilizing Lee's fuzzy time series method, the predicted JCI closing price for the
following period is 7,046, with a MAPE value of 3.10 percent. It can be concluded from the forecasting results of the
Chen and Lee methods that the Lee model FTS is superior to the Chen model FTS in predicting the JCI closing price.