Analysis and Prediction of Stock Price using HMM and Facebook’s Prophet Computational Models

Q4 Mathematics
K. Senthamarai Kannan
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Abstract

In recent times, the utilization of Statistical and Machine Learning techniques has gained prominence in the realm of financial data analysis. These methods are applied to various types of financial data, encompassing textual information, numerical data, and graphical representations. This study aims to compare the performance of two prominent forecasting methods, Hidden Markov Models and Facebook’s Prophet in the context of stock price prediction. Assessing the predictive accuracy, interpretability, and adaptability of both approaches through empirical experiments and case studies sheds light on their respective advantages and limitations. These experiments demonstrate that the predicted stock prices are in closer proximity to the actual price when compared to using a single data source. Furthermore, the achieved MAPE are 0.01, 0.025 and respectively, outperforming conventional methodologies. Our validation of effectiveness extends to real-world datasets encompassing the NIFTY50 Index. These findings offer valuable insights for researchers and practitioners seeking effective strategies for stock price prediction.
使用 HMM 和 Facebook 的先知计算模型分析和预测股票价格
近来,统计和机器学习技术在金融数据分析领域的应用日益突出。这些方法适用于各种类型的金融数据,包括文本信息、数字数据和图形表示。本研究旨在比较隐马尔可夫模型和 Facebook's Prophet 这两种著名预测方法在股价预测方面的表现。通过实证实验和案例研究来评估这两种方法的预测准确性、可解释性和适应性,从而揭示它们各自的优势和局限性。这些实验表明,与使用单一数据源相比,预测的股票价格更接近实际价格。此外,实现的 MAPE 分别为 0.01、0.025 和 0.025,优于传统方法。我们对有效性的验证扩展到了包括 NIFTY50 指数在内的真实世界数据集。这些发现为寻求有效股价预测策略的研究人员和从业人员提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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