Abstract
Abstract: Stock market forecasting is a tough mission because of its complex and dynamic nature. Deep learning models have recently been proven to be successful at stock market predictions. Traditional deep learning models, however, frequently disregard the hierarchical structure and temporal relationships of the stock market. Here, we introduce the StockTensor, a brand-new tensorized hierarchical graph attention network for stock market forecasting. StockTensor models the hierarchical structure of the stock market data by constructing a hierarchical graph of stocks. At each level of the hierarchy, StockTensor uses a graph attention network to learn the relationships between stocks and aggregate the information from neighboring stocks. StockTensor also models the temporal dependencies of the stock market data by using a recurrent neural network. The recurrent neural network learns to predict future stock prices based on the current stock prices and the historical stock prices. We evaluate StockTensor on two real-world stock market datasets. The results show that StockTensor outperforms several stateof-the-art stock market forecasting models.
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