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examples/benchmarks/ALSTM/README.md
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- ALSTM contains a temporal attentive aggregation layer based on normal LSTM. - ALSTM contains a temporal attentive aggregation layer based on normal LSTM.
- The code used in Qlib is a pyTorch implementation of Code: https://github.com/fulifeng/Adv-ALSTM
- Paper: A dual-stage attention-based recurrent neural network for time series prediction. - Paper: A dual-stage attention-based recurrent neural network for time series prediction.
https://www.ijcai.org/Proceedings/2017/0366.pdf [https://www.ijcai.org/Proceedings/2017/0366.pdf](https://www.ijcai.org/Proceedings/2017/0366.pdf)

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examples/benchmarks/SFM/README.md
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# State-Frequency-Memory # State-Frequency-Memory
- State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions. - State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform to decompose the hidden states of memory cells and capture the multi-frequency trading patterns from past market data to make stock price predictions.
- The code used in Qlib is a pyTorch implementation of SFM.
- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.) - Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. [https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.](https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf.)