Advances in deep neural network (DNN) architectures have enabled new
prediction techniques for stock market data. Unlike other multivariate
time-series data, stock markets show two unique characteristics: (i)
\emph{multi-order dynamics}, as stock prices are affected by strong
non-pairwise correlations (e.g., within the same industry); and (ii)
\emph{internal dynamics}, as each individual stock shows some particular
behaviour. Recent DNN-based methods capture multi-order dynamics using
hypergraphs, but rely on the Fourier basis in the convolution, which is both
inefficient and ineffective. In addition, they largely ignore internal dynamics
by adopting the same model for each stock, which implies a severe information
loss.
In this paper, we propose a framework for stock movement prediction to
overcome the above issues. Specifically, the framework includes temporal
generative filters that implement a memory-based mechanism onto an LSTM network
in an attempt to learn individual patterns per stock. Moreover, we employ
hypergraph attentions to capture the non-pairwise correlations. Here, using the
wavelet basis instead of the Fourier basis, enables us to simplify the message
passing and focus on the localized convolution. Experiments with US market data
over six years show that our framework outperforms state-of-the-art methods in
terms of profit and stability. Our source code and data are available at
https://github.com/thanhtrunghuynh93/estimate.

本研究提出了一个用于预测股票走势的框架，利用 DNN 技术实现了逐股票的内部动态与不同股票间多阶相关性建模，并采用了基于小波基的超图注意力，实现了在效率与准确率上的平衡。实验结果表明，该框架在收益和稳定性方面超越了现有方法。

股票运动预测中多种动力学和内部动力学的高效集成

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Convolutional neural networks (CNN) are generally designed with a heuristic
initialization of network architecture and trained for a certain task. This
often leads to overparametrization after learning and induces redundancy in the
information flow paths within the network. This robustness and reliability is
at the increased cost of redundant computations. Several methods have been
proposed which leverage metrics that quantify the redundancy in each layer.
However, layer-wise evaluation in these methods disregards the long-range
redundancy which exists across depth on account of the distributed nature of
the features learned by the model. In this paper, we propose (i) a mechanism to
empirically demonstrate the robustness in performance of a CNN on account of
redundancy across its depth, (ii) a method to identify the systemic redundancy
in response of a CNN across depth using the understanding of unit impulse
response, we subsequently demonstrate use of these methods to interpret
redundancy in few networks as example. These techniques provide better insights
into the internal dynamics of a CNN

本文提出了利用冗余性展示卷积神经网络性能稳定性的机制和利用单位脉冲响应理解反应的冗余性的方法，并提供了在几个网络上展示冗余性的示例，这些技术可以更好地理解卷积神经网络的内部动态。