The stock forecast is one of the most challenging tasks that have attracted numerous economists and scientists worldwide. Stock prices can be affected by many reasons, such as physiological, rational, and irrational behavior. Such factors can combine to make the prices volatile and very challenge to predict with great accuracy for a long time in numerous cases. In this study, we have deployed a long short-term memory architecture with various time-steps and classic machine learning methods such as random forests, support vector machines, and autoregression on the Taiwanese stock market collected in 14–15 years. As shown from the visual results, the predicted values have followed the patterns as the actual prices with low error rates in various metrics, including root mean square error and mean absolute error. This work is expected to provide a valuable tool for investigating stock price patterns of stock markets in the future.