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A Labeling Method for Financial Time Series Prediction Based on Trends
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597331/ https://www.ncbi.nlm.nih.gov/pubmed/33286931 http://dx.doi.org/10.3390/e22101162 |
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author | Wu, Dingming Wang, Xiaolong Su, Jingyong Tang, Buzhou Wu, Shaocong |
author_facet | Wu, Dingming Wang, Xiaolong Su, Jingyong Tang, Buzhou Wu, Shaocong |
author_sort | Wu, Dingming |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7597331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75973312020-11-09 A Labeling Method for Financial Time Series Prediction Based on Trends Wu, Dingming Wang, Xiaolong Su, Jingyong Tang, Buzhou Wu, Shaocong Entropy (Basel) Article 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. MDPI 2020-10-15 /pmc/articles/PMC7597331/ /pubmed/33286931 http://dx.doi.org/10.3390/e22101162 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Dingming Wang, Xiaolong Su, Jingyong Tang, Buzhou Wu, Shaocong A Labeling Method for Financial Time Series Prediction Based on Trends |
title | A Labeling Method for Financial Time Series Prediction Based on Trends |
title_full | A Labeling Method for Financial Time Series Prediction Based on Trends |
title_fullStr | A Labeling Method for Financial Time Series Prediction Based on Trends |
title_full_unstemmed | A Labeling Method for Financial Time Series Prediction Based on Trends |
title_short | A Labeling Method for Financial Time Series Prediction Based on Trends |
title_sort | labeling method for financial time series prediction based on trends |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597331/ https://www.ncbi.nlm.nih.gov/pubmed/33286931 http://dx.doi.org/10.3390/e22101162 |
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