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A Labeling Method for Financial Time Series Prediction Based on Trends

The College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
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Received: 23 September 2020 / Revised: 11 October 2020 / Accepted: 13 October 2020 / Published: 15 October 2020
Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data. View Full-Text
Keywords: financial time series; stock prediction; machine learning; labeling method; deep learning financial time series; stock prediction; machine learning; labeling method; deep learning
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MDPI and ACS Style

Wu, D.; Wang, X.; Su, J.; Tang, B.; Wu, S. A Labeling Method for Financial Time Series Prediction Based on Trends. Entropy 2020, 22, 1162. https://0-doi-org.brum.beds.ac.uk/10.3390/e22101162

AMA Style

Wu D, Wang X, Su J, Tang B, Wu S. A Labeling Method for Financial Time Series Prediction Based on Trends. Entropy. 2020; 22(10):1162. https://0-doi-org.brum.beds.ac.uk/10.3390/e22101162

Chicago/Turabian Style

Wu, Dingming, Xiaolong Wang, Jingyong Su, Buzhou Tang, and Shaocong Wu. 2020. "A Labeling Method for Financial Time Series Prediction Based on Trends" Entropy 22, no. 10: 1162. https://0-doi-org.brum.beds.ac.uk/10.3390/e22101162

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