This study mainly optimizes the structure and parameters of the LSTM model, so that the accuracy of predicting the trend in stock prices is significantly improved. Besides, we investigate the weekly and daily data on US stock in terms of predicting the LSTM neural network. On one hand, we compare the difference to verify the impact of different data sets on the forecast. On the other hand, we provide selection suggestions for data sets so as to increase the accuracy of stock forecast. This study uses the multi-time-series stock forecast in the improved LSTM model to predict the trend in stock prices. The results demonstrate that the weekly data perform better in forecast than daily data. To be specific, the average accuracy of daily data and weekly data is 52.8% and 58%, respectively. In summary, the application of weekly data to training the LSTM model yields higher accuracy in stock forecast.