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Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. Table of contents Models Agents Realtime Agent Data Explorations Simulations Tensorflow-js Misc Results Results Agent Results signal prediction Results analysis Results sim...
Modify the lstm and transformer models to support mixed length input. 在transformer模型,增加了 decoder层,期望增加预测精度 Added a decoder layer in the transformer model, hoping to increase the prediction accuracy. 20230327 修改了部分运行逻辑,配合load pkl预处理文件,极大的提高了训练速度 ...
2.2 股票回报预测模型 (Stock Return Prediction Models) 降维模型:使用主成分分析(PCA)等技术简化数据集,保留关键信息。 线性模型:使用带有惩罚项的模型,如LASSO,以减少噪声信息,提高预测精度。 非线性模型:基于历史数据拟合预测变量和回报之间的非线性结构,使用人工智能算法如随机森林、模糊神经网络和长短期记忆(LSTM)...
In this work, an attempt is made for prediction of stock market trend. Two models are built one for daily prediction and the other one is for monthly prediction. Supervised machine learning algorithms are used to build the models. As part of the daily prediction model, historical prices are ...
In this article, the authors survey the bond-stock prediction model for five worldwide equity markets. This model is useful for predicting the time-varying equity risk premium (ERP) and for strategic asset allocation of bond-stock equity mixes. The focus is on the model's economic and financi...
Stock Exchange Prediction using neural networks has been an interesting research problem whereby many researchers have developed a keen interest in prediction of future values and trends. Little research has been done to apply and improve prediction models based on newer and impactful variables to show...
In recent years, the prediction technology of artificial intelligence models has made a significant breakthrough. Compared with the traditional linear regression method, artificial intelligence algorithm has strong processing ability for high-dimensional, low signal-to-noise ratio, nonlinear and non-stationa...
使用机器学习进行股票预测并指导短线(预测未来3日股价)交易。. Contribute to lxj0276/ML-stock-prediction-models development by creating an account on GitHub.