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In an era where financial markets are as dynamic as ever, the ability to predict stock prices accurately is of paramount importance. This research delves into the realm of stock price forecasting, leveraging the power of time series analysis and machine learning models. The study explores the effectiveness of various predictive methodologies, including ARIMA, random forests, support vector machines, and neural networks, in capturing the intricate patterns of financial markets. We present a comprehensive comparative analysis of these models, shedding light on their respective strengths and weaknesses. By examining their performance, we offer valuable insights that can inform financial decision-makers and analysts. This paper not only advances our understanding of stock price prediction but also points to promising avenues for enhancing investment strategies in a fast-paced, ever-evolving financial landscape.

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Stock Price Forecasting: A Comparative Analysis of Time Series and Machine Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Imen Ferjani,
  • Fatma Mallouli,
  • Imen Gmach,
  • Nada Chaabane,
  • Houda Chamakhi,
  • Mejda Dakhlaoui,
  • Hana Faltakh

摘要

In an era where financial markets are as dynamic as ever, the ability to predict stock prices accurately is of paramount importance. This research delves into the realm of stock price forecasting, leveraging the power of time series analysis and machine learning models. The study explores the effectiveness of various predictive methodologies, including ARIMA, random forests, support vector machines, and neural networks, in capturing the intricate patterns of financial markets. We present a comprehensive comparative analysis of these models, shedding light on their respective strengths and weaknesses. By examining their performance, we offer valuable insights that can inform financial decision-makers and analysts. This paper not only advances our understanding of stock price prediction but also points to promising avenues for enhancing investment strategies in a fast-paced, ever-evolving financial landscape.