This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased.

翻译：本文提出利用机器学习算法结合超级预测者预测构建价格预测模型，旨在提升投资决策质量。研究构建了五种机器学习模型，包括双向LSTM、ARIMA、CNN与LSTM组合模型、GRU模型，以及采用LSTM与GRU算法构建的混合模型。通过平均绝对误差指标评估各模型的预测精度。此外，本文建议通过识别超级预测者并追踪其预测记录来融合人类智能，以预判股价的不可预测波动。当这些用户的预测与机器学习及自然语言处理技术相结合时，可进一步提升股价预测的准确性。任何商品的价格预测都是重要课题，而股市股价预测面临更大的不确定性。针对部分投资者股票知识有限、接触市场经验不足的现状，本文提出采用机器学习算法构建价格预测模型。本研究构建了五种机器学习模型：双向LSTM、ARIMA、CNN-LSTM组合模型、GRU模型，以及LSTM-GRU混合模型。随后通过MAE评分评估各模型，以确定预测精度最高的模型。除技术模型外，本文还建议运用人类智能来精准预测股市价格模式变化，核心目标是识别超级预测者并追踪其预测记录，从而预判股价的不可预测波动。通过充分发挥机器学习与人类智能的协同效应，可显著提升预测准确度。