Since the beginning of stock trading, investors and researchers have tried to find effective ways to predict the direction of stock prices on the next day. However, predicting stock prices is a hard task because there exist many factors that may affect the next day’s stock prices. Recently, investors and researchers have adopted machine learning techniques with technical indicator analysis to make prediction. But the prediction accuracy is unsatisfactory. Thus, this study aims to examine the problem of stock price prediction with time series and proposes an effective way to filter out the datasets, which consists of three key steps: First, a time series model with long-short-term memory (LSTM) was used to identify the possible problems and solve them in the process of stock price prediction. Second, the results of LSTM were adopted to influence the stock trend prediction by a variety of machine learning techniques, including random forest, support vector machine, light gradient boosting machine (LightGBM). Third, a novel method was proposed to select suitable datasets through buying or selling. Hereby, FTSE TWSE Taiwan 50 Index stocks were used as training and testing datasets, respectively. Some important days were selected for prediction and decision making. The experimental results show that the highest prediction accuracy of 86% and the average prediction accuracy of 82% have been obtained. Consequently, when comparing to other existing methods, the accuracy of predicting stock price trend with our proposed approach has been significantly improved.