A Comparative Analysis of Machine Learning Models for Time Series Forecasting in Finance

Authors

  • Noraini Abu Talib Universiti Teknologi Malaysia (UTM)
  • Rafiq Ahmad Universiti Teknologi Malaysia (UTM)
  • Siti Norbaya Noor Universiti Teknologi Malaysia (UTM)

DOI:

https://doi.org/10.62951/ijamc.v1i2.71

Keywords:

Time series forecasting, Machine learning, ARIMA, LSTM, Financial analysis.

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.

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Published

2024-04-30

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