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

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.