Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

By Liheng Zhang, Charu C. Aggarwal, Guo-Jun Qi. 2017

498 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Weiwei Jiang

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Citations in the corpus

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, background): “In most recent time, Zhang et al. [28] proposed a new RNN, called State Frequency Memory (SFM), to discover multi-frequency trading patterns for stock price prediction.”
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, background): “It is intractable to distinguish the contribution of individual variables into the prediction through the sequence of hidden states (Zhang et al., 2017).”
Neural Networks Fail to Learn Periodic Functions and How to Fix It (2020, background): “…are dictated by periodic motions in the Solar System [23]; the human body has an intrinsic biological clock that is periodic in nature [18, 31], the number of passengers on the metro follows daily and weekly modulations, and the stock market also experiences (semi-)periodic ﬂuctuations [25, 39].”
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting (2023, methodology): “Specifically, SFM [29] decomposes the hidden state of LSTM into frequencies by Discrete Fourier Transform (DFT).”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…al., 2018; Al-Thelaya et al., 2019; de A. Arajo et al., 2019; Cao et al., 2019; Cao & Wang, 2019; Ding & Qin, 2019; Lee et al., 2019; Li et al., 2019b; Sachdeva et al., 2019; Sethia & Raut, 2019; Tang et al., 2019), or between -1 and 1 (Ticknor, 2013; Zhang et al., 2017b; Sezer & Ozbayoglu, 2018).”
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective (2023, unknown): “For instance, SFM [24] decomposes the hidden state of LSTM into multiple frequencies by Discrete Fourier Transform (DFT). mWDN [25] decomposes the time series into multilevel sub-series by discrete wavelet decomposition (DWT) and feeds them to LSTM network.”
Neural Networks and Deep Learning (2018, unknown): “The vast majority of work on recurrent networks has been on symbolic data, although there is also some work on real-valued time series [80, 101, 559].”