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Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

2018; IOP Publishing; Volume: 971; Linguagem: Inglês

10.1088/1742-6596/971/1/012017

1742-6596

Rian Febrian Umbara, Dede Tarwidi, Erwin Budi Setiawan,

Time Series Analysis and Forecasting

The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.