用改进Transformer模型预测股票多变量时序，提升预测精度。
Transformer Based Time-Series Forecasting for Stock
- 用改进版Transformer（Stockformer）建模多变量股票时序
- 通过注意力机制捕捉多维度市场动态关联性
- 适合量化交易与金融时间序列研究者参考
从表面看，股价被认为是混乱、动态且难以预测的。这是一项极具挑战性的任务，全球数亿散户和专业交易员每秒都在尝试预判，甚至在市场开盘前就已开始。随着机器学习的发展以及市场多年积累的数据量不断增长，应用深度学习等技术已成为必然趋势。本文将该问题建模为多变量预测任务，而非简单的自回归模型。通过我们提出的改良版Transformer——Stockformer，利用注意力机制实现多变量分析。
原文摘要 · Abstract (English)
To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks that hundreds of millions of retail traders and professional traders around the world try to do every second even before the market opens. With recent advances in the development of machine learning and the amount of data the market generated over years, applying machine learning techniques such as deep learning neural networks is unavoidable. In this work, we modeled the task as a multivariate forecasting problem, instead of a naive autoregression problem. The multivariate analysis is done using the attention mechanism via applying a mutated version of the Transformer, "Stockformer", which we created.
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