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For investors, accurately predicting stock market prices is a critical part of financial analysis. This work studies a wide variety of machine learning algorithms specifically designed for the task of predicting stock prices using a variety of techniques and the latest technologies available as of the time of study. The study critically compares a suite of algorithms, including Support Vector Regression (SVR), Random Forests, Decision Tree models, and Long Short-Term Memory (LSTM), each with differing strengths. Moreover, it investigates a variety of approaches that are focused on understanding the complex connections found in the past price data. The dataset used for this study includes extremely long-term stock price representatives over a time range from 2010 to 2024 for Tata Consultancy services (TCS), containing a wide range of information, including opening and closing trading prices, trading volumes, and a variety of calculated indicators reflecting market behaviour. To understand the first experiment carried out for this study, it can be seen that in combination mode, for the best-performing model, Random Forest has the best interpretability. In addition, both Support Vector Regression (SVR) and Decision Tree algorithms deliver both impressive short-term prediction results and clear explanations behind their decisions.

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Dynamic Stock Price Forecasting Using Machine Learning and Real-Time Data Integration

  • 会议论文

  • 发表日期：

    2026年

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  • Parampreet Kaur,
  • Narayan Gupta,
  • Pawan Kumar,
  • Prince Kumar Singh,
  • Priyabart Kumar

摘要

For investors, accurately predicting stock market prices is a critical part of financial analysis. This work studies a wide variety of machine learning algorithms specifically designed for the task of predicting stock prices using a variety of techniques and the latest technologies available as of the time of study. The study critically compares a suite of algorithms, including Support Vector Regression (SVR), Random Forests, Decision Tree models, and Long Short-Term Memory (LSTM), each with differing strengths. Moreover, it investigates a variety of approaches that are focused on understanding the complex connections found in the past price data. The dataset used for this study includes extremely long-term stock price representatives over a time range from 2010 to 2024 for Tata Consultancy services (TCS), containing a wide range of information, including opening and closing trading prices, trading volumes, and a variety of calculated indicators reflecting market behaviour. To understand the first experiment carried out for this study, it can be seen that in combination mode, for the best-performing model, Random Forest has the best interpretability. In addition, both Support Vector Regression (SVR) and Decision Tree algorithms deliver both impressive short-term prediction results and clear explanations behind their decisions.