CGSTAE learns correlation graphs with spatial self-attention, derives causal graphs via a three-step invariance algorithm, and uses GCLSTM encoder-decoder to monitor industrial processes on Tennessee Eastman and air separation data.
A comprehensive survey on graph neural networks,
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An integrated node transformer and BERT sentiment model reports 0.80% MAPE for one-day stock predictions on 20 S&P 500 stocks, beating ARIMA and LSTM baselines.
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Graph Autoencoder for Process Monitoring
CGSTAE learns correlation graphs with spatial self-attention, derives causal graphs via a three-step invariance algorithm, and uses GCLSTM encoder-decoder to monitor industrial processes on Tennessee Eastman and air separation data.
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Stock Market Prediction Using Node Transformer Architecture Integrated with BERT Sentiment Analysis
An integrated node transformer and BERT sentiment model reports 0.80% MAPE for one-day stock predictions on 20 S&P 500 stocks, beating ARIMA and LSTM baselines.