📅 30 April 2024
DOI: 10.62951/ijamc.v1i2.71
A Comparative Analysis of Machine Learning Models for Time Series Forecasting in Finance
International Journal of Applied Mathematics and Computing
Asosiasi Riset Ilmu Matematika dan Sains Indonesia (ARIMSI)
📄 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.
🔖 Keywords
#Time series forecasting; Machine learning; ARIMA; LSTM
#Financial analysis.
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 April 2024
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2024
📝 HOW TO CITE
Noraini Abu Talib; Rafiq Ahmad; Siti Norbaya Noor, "A Comparative Analysis of Machine Learning Models for Time Series Forecasting in Finance," International Journal of Applied Mathematics and Computing, vol. 1, no. 2, Apr. 2024.
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