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Through in-depth analysis of ultra high frequency (UHF) stock price changedata, more reasonable discrete dynamic distribution models are constructed inthis paper. Firstly, we classify the price changes into several categories.Then, temporal convolutional network (TCN) is utilized to predict theconditional probability for each category. Furthermore, attention mechanism isadded into the TCN architecture to model the time-varying distribution forstock price change data. Empirical research on constituent stocks of ChineseShenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework modeland the TCN (attention) framework have a better overall performance than GARCHfamily models and the long short-term memory (LSTM) framework model for thedescription of the dynamic process of the UHF stock price change sequence. Inaddition, the scale of the dataset reached nearly 10 million, to the best ofour knowledge, there has been no previous attempt to apply TCN to such alarge-scale UHF transaction price dataset in Chinese stock market.

Price change prediction of ultra high frequency financial data based on temporal convolutional network