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15 lines
851 B
Markdown
15 lines
851 B
Markdown
## Requirement
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* pandas==1.1.2
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* numpy==1.17.4
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* scikit_learn==0.23.2
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* torch==1.7.0
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## HATS
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* HATS is a a hierarchical attention network for stock prediction which uses relational data for stock market prediction. HATS selectively aggregates information
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on different relation types and adds the information to the representations of each company. HATS is used as a relational modeling module with initialized node representations.Furthermore, HATS
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can predict not only individual stock prices but also market index movements, which is similar to the graph classification task.
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* HATS uses pretrained model of GRU and LSTM. The code of GRU and LSTM used in Qlib is a pyTorch implemention of GRU and LSTM.
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* Paper address:HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction https://arxiv.org/pdf/1908.07999.pdf |