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:利用机器学习和人工神经网络工具和技术对股票市场预测进行了彻底调查,这是一个典型的问题。这个问题的有趣方面包括时间依赖及其波动性和其他复杂关系。为了将它们结合起来,利用隐藏的马克罗夫模型(HMM)来预测股票价格。我们根据以前的数据展示了第二天预测股票价格的最大Aposeriori(MAP)HMM方法。一个HMM通过分析股票价格的微小变化以及每日内高低值来培训,然后用来对第二天所有可能的股票价格作出MAP估计。我们在工作中显示的方法相当普遍,可以用来预测任何公司的股票价格,因为HMMM是用该公司储备数据集的数据集来训练的。我们用一些广泛使用的精确指标来评估我们的模型的准确性,以解决回归问题,并得出令人满意的结果。