基于时间卷积网络的超高频金融数据价格变化预测
Price change prediction of ultra high frequency financial data based on temporal convolutional network
摘要：
通过对超高频股价变动数据的深入分析，构建了更为合理的离散动态分布模型。首先，我们将价格变化分为几类。然后，时间卷积网络（TCN）被用来预测每个类别的条件概率。此外，在TCN架构中加入了注意机制，对股价变化数据的时变分布进行建模。对中国深圳证券交易所100指数成分股的实证研究发现，TCN框架模型和TCN（attention）框架模型在描述UHF股价变动序列动态过程方面的整体表现优于Gestival族模型和长短期记忆（LSTM）框架模型。此外，数据集的规模达到近千万，据我们所知，此前还没有将TCN应用于中国股票市场如此大规模的UHF交易价格数据集的尝试。
英文摘要：
Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
