论文标题
CS-NET：预测高维特征空间数据的时间序列的结构方法，观察到有限
cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations
论文作者
论文摘要
近年来，已经引入了基于深度学习的方法来解决与预测有关的问题。这些新型方法在单变量和低维的多元时间序列预测任务中表现出了令人印象深刻的性能。但是，当这些新颖的方法用于处理高维多元预测问题时，它们的性能受到实用训练时间和合理的GPU记忆配置的高度限制。在本文中，受希尔伯特领域的基础变化的启发，我们提出了一种灵活的数据提取技术，该技术在高维多元预测任务中表现出色。我们的方法最初是为国家科学基金会（NSF）算法开发的，用于威胁检测（ATD）2022挑战。使用注意机制和卷积神经网络（CNN）体系结构实施，我们的方法表现出了出色的性能和兼容性。我们在GDELT数据集上训练的模型在ATD Sprint系列中完成了第一名和第二名，并为时间序列预测的其他数据集提供了希望。
In recent years, deep-learning-based approaches have been introduced to solving time-series forecasting-related problems. These novel methods have demonstrated impressive performance in univariate and low-dimensional multivariate time-series forecasting tasks. However, when these novel methods are used to handle high-dimensional multivariate forecasting problems, their performance is highly restricted by a practical training time and a reasonable GPU memory configuration. In this paper, inspired by a change of basis in the Hilbert space, we propose a flexible data feature extraction technique that excels in high-dimensional multivariate forecasting tasks. Our approach was originally developed for the National Science Foundation (NSF) Algorithms for Threat Detection (ATD) 2022 Challenge. Implemented using the attention mechanism and Convolutional Neural Networks (CNN) architecture, our method demonstrates great performance and compatibility. Our models trained on the GDELT Dataset finished 1st and 2nd places in the ATD sprint series and hold promise for other datasets for time series forecasting.
