From c52b84b3fe7423125fb8373ea78d4f9bf7f8734c Mon Sep 17 00:00:00 2001 From: lwwang1995 Date: Fri, 27 Nov 2020 22:50:07 +0800 Subject: [PATCH] Fix sfm readme. --- examples/benchmarks/SFM/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/benchmarks/SFM/README.md b/examples/benchmarks/SFM/README.md index 06ca50485..eb1c8b157 100644 --- a/examples/benchmarks/SFM/README.md +++ b/examples/benchmarks/SFM/README.md @@ -1,4 +1,4 @@ # State-Frequency-Memory -- State Frequency Memory (SFM) is a novel recurrent network that uses Discrete Fourier Transform (DFT) 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 (Zhang, L., Aggarwal, C., & Qi, G. J. (2017,)). -- Paper: Stock Price Prediction via Discovering Multi-Frequency Trading Patterns. https://www.cs.ucf.edu/~gqi/publications/kdd2017_stock.pdf. \ No newline at end of file +- 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.) \ No newline at end of file