  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.
