Stock market forecasting is a classic problem that has been thoroughly investigated using machine learning and artificial neural network based tools and techniques. Interesting aspects of this problem include its time reliance as well as its volatility and other complex relationships. To combine them, hidden markov models (HMMs) have been utilized to anticipate the price of stocks. We demonstrated the Maximum A Posteriori (MAP) HMM method for predicting stock prices for the next day based on previous data. An HMM is trained by analyzing the fractional change in the stock price as well as the intraday high and low values. It is then utilized to produce a MAP estimate across all possible stock prices for the next day. The approach demonstrated in our work is quite generalized and can be used to predict the stock price for any company, given that the HMM is trained on the dataset of that company's stocks dataset. We evaluated the accuracy of our models using some extensively used accuracy metrics for regression problems and came up with a satisfactory outcome.

翻译：股票市场预测是一个经典问题，基于机器学习与人工神经网络的技术手段已对此进行了深入研究。该问题的关键特征包括其时间依赖性、波动性及其他复杂关联性。为综合应对这些特征，隐马尔可夫模型已被用于股票价格预测。我们展示了基于最大后验概率的隐马尔可夫模型方法，利用历史数据预测次日的股票价格。该模型通过分析股票价格的分数变化以及日内最高价与最低价进行训练，随后对所有可能的次日股票价格生成最大后验概率估计。本文提出的方法具有高度通用性，只要对特定公司的股票数据集训练隐马尔可夫模型，即可用于预测该公司的股票价格。我们采用回归问题中广泛使用的精度评估指标对模型准确率进行验证，并取得了令人满意的结果。