Work

Implementation of Fuzzy Time Series Markov Chain Method using Kernel Smoothing in forecasting the Stock Price of PT. Elnusa Tbk.

Year: 2025

Type: article

Abstract: This research aims to apply the Fuzzy Time Series Markov Chain combined with Kernel Smoothing in forecasting stock prices. The Kernel Smoothing technique is used to smooth stock data before the fuzzification process, resulting in more accurate predictions. The research stages include Data Smoothing, Fuzzy interval formation, Fuzzy Logical Relationship and Fuzzy Logical Relationship Group formation, and forecasting using Markov Chain Transition Matrix. Evaluation using MAPE shows a low prediction error rate, with a value of 0.005974257%, so this method is effective for volatile stock data. The implementation of this model is expected to be a reference for investors and analysts in understanding and predicting future stock price movements.

Source: Indonesian Journal of Computational and Applied Mathematics

DOI: 10.64182/indocam.v1i1.9

Authors: Marcela Mokodompit, Salmun K Nasib, Ismail Djakaria, Nisky Imansyah Yahya, Isran K. Hasan

Institution: State University of Gorontalo

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4411468915

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