| Summary: | 碩士 === 開南大學 === 資訊學院碩士在職專班 === 106 === This study takes the TensorFlow as a backend engine for deep learning. The Multi-Layer Perceptron (MLP) is built to solve the time series forecasting problems. The case study are daily stock closing prices in Taiwan, i.e. the Taiwan Semiconductor Manufacturing Company Limited (TSMC), Uni-President Enterprises Corporation (Uni-President), and Largan Precision Company Limited (LARGAN Precision). We collect 120 daily records from 2017/01/03 to 2017/07/04. Around 20 input features we used are: the Trade Volume, the Trade Value, the Opening Price, the Highest Price, the Lowest Price, the Closing Price, the Delta Price, the Transaction amount, and other Technical Analysis Indicators. Then, the Stepwise Regression Analysis is adopted as a filter for screening out some input features really correlative to the Label (the predict closing price). The numerical results are summarized as follows: the Mean Absolute Percentage Error (MAPE) and Standard Deviation (SD) in training and predicting stages for the TSMC are (0.17%, 0.06) and (0.33%, 0.05); (0.15%, 0.06) and (0.20%, 0.04) for the Uni-President; (0.35%, 0.10) and (0.37%, 0.05) for the LARGAN Precision.
Keywords: Deep learning, Time series, TensorFlow, Stock price prediction
|
|---|
