Abstract: Abstract: With the development of society and information technology， financial instruments and stock transactions have taken on a new form， namely， the number of financial data increases. Therefore， stock trend prediction is particularly important in high-frequency trading. Stock trend prediction in high-frequency trading is particularly important to improve the accuracy of stock trend prediction in high-frequency trading. A temporal convolutional network （TCN） model based on dynamic time warping （DTW） clustering analysis is proposed. In the model， the opening price， the highest price， the lowest price， the closing price， the trading volume， and the trading volume are used as the stock characteristic variables. In order to avoid the influence of magnitude， the feature vector is standardized first， and then the stock is classified by using the dynamic time warping to measure the similarity of time series， Then， temporal convolutional network （TCN） extracts the common characteristics of the categories to predict the opening and closing price trends of the stocks of the categories， and compares them with the actual trends. The experiment is conducted with the minute-level data of 19 industry universal stocks. Compared with traditional time series model and LSTM network model， it has greater time characteristics. The results show that the model can effectively classify the stocks with the same trend into the same category， and achieve accurate trend prediction in the minute-level high-frequency trading.

Key words: Key words: time series forecasting, dynamic time warping, Temporal convolutional networks, high-frequency trading, cluster analysis