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All Journal International Journal of Applied Mathematics and Computing.
Noraini Abu Talib
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Computer Science & IT Mathematics

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A Comparative Analysis of Machine Learning Models for Time Series Forecasting in Finance Noraini Abu Talib; Rafiq Ahmad; Siti Norbaya Noor
International Journal of Applied Mathematics and Computing Vol. 1 No. 2 (2024): April : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i2.71

Abstract

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.
Co-Authors Rafiq Ahmad Siti Norbaya Noor