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Does Holt-Winters Seasonality Fare Better Against Fuzzy Time Series in Forecasting Stock Index?
Abstract
The purpose of this research is to compare the forecasting performance of Holt-Winter seasonality and Fuzzy Time Series Forecasting-Chen Model and to determine which method performs best. Holt-Winter seasonality was divided into additive and multiplicative Holt-Winters. The forecast object was IDXV30, that was the stock index of 30 lowest valued stocks with good liquidity and performance. The stock index was biweekly stock index beginning from August 2019 until September 2022. The result indicated that Holt-Winters Additive model has the best forecast accuracy, followed by Fuzzy Time Series-Chen Model and Holt-Winters Multiplicative model. The Mean Absolute Percentage Error (MAPE) of Holt-Winters additive model was 2.0982%, while Fuzzy Time Series-Chen model was 3.1471%. The MAPE for Holt-Winters multiplicative model was 10.47425. The implication of the research is that the time series econometrics model, in this case Holt-Winters Seasonality, is still a very powerful model for forecasting stock index in Indonesian Stock Exchange.
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