With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. Kalman filters are recursive and use a feedback mechanism to perform error correction. This correction makes them best suited for making accurate predictions as they can factor in the market volatility, whereas XGBoost is a promising technique for datasets that are nonlinear and can gather knowledge by detecting patterns and relationships in the data. XGBoost is also capable of capturing the time dependency of features efficiently. ARIMA refers to an Auto Regressive Integrated Moving Average model that has become very popular in recent times. It is mostly used on time series data and works by eliminating its stationarity. Finally, a hybrid model combining Kalman filters and XGBoostis discussed and a comparison of the results of each of the four models, are made to provide a better clarity for making investments by forecasting the price of a stock.

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使用统计和机器学习模型预测股票价格：比较分析

随着机器学习的出现，人们提出了许多预测股票价格的方法。迄今为止，已经开发了各种模型，例如循环神经网络、长短期记忆、卷积神经网络滑动窗口等，但都不够准确。在这里，目的是预测股票的价格，并比较使用三种主要算法获得的结果，即卡尔曼滤波器、XGBoost 和 ARIMA。卡尔曼滤波器是递归的，并使用反馈机制来执行纠错。这种修正使它们最适合做出准确的预测，因为它们可以考虑市场波动，而 XGBoost 是一种很有前途的技术，适用于非线性数据集，可以通过检测数据中的模式和关系来收集知识。XGBoost 还能够有效地捕捉特征的时间依赖性。ARIMA 指的是一种近来非常流行的自回归综合移动平均模型。它主要用于时间序列数据，并通过消除其平稳性来工作。最后，讨论了结合卡尔曼滤波器和 XGBoost 的混合模型，并比较了四个模型中每一个模型的结果，以通过预测股票价格为投资提供更好的清晰度。