Time Series Trend Analysis Based on K-Means and Support Vector Machine


  • Van Vo Faculty of Information Technology, Industrial University of Ho Chi Minh City
  • Jiawei Luo School of Information Science and Engineering, Hunan University
  • Bay Vo Ton Duc Thang University, Ho Chi Minh City


Machine learning, time series trend analysis, support vector machines, k-means clustering


In this paper, we apply both supervised and unsupervised machine learning techniques to predict the trend of financial time series based on trading rules. These techniques are K-means for clustering the similar group of data and support vector machine for training and testing historical data to perform a one-day-ahead trend prediction. To evaluate the method, we compare the proposed method with traditional back-propagation neural network and a standalone support vector machine. In addition, to implement this combination method, we use the financial time series data obtained from Yahoo Finance website and the experimental results also validate the effectiveness of the method.


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How to Cite

Vo, V., Luo, J., & Vo, B. (2016). Time Series Trend Analysis Based on K-Means and Support Vector Machine. COMPUTING AND INFORMATICS, 35(1), 111–127. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/1445