Abstract
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.
Keywords
MSC
68-T05
Cite this article
@article{cai-1445,
author = {Van Vo and Jiawei Luo and Bay Vo},
title = {Time Series Trend Analysis Based on K-Means and Support Vector Machine},
journal = {Computing and Informatics},
volume = {35},
number = {1},
pages = {111--127},
year = {2016},
issn = {1335-9150},
url = {https://cai.type.sk/content/2016/1/time-series-trend-analysis-based-on-k-means-and-support-vector-machine/}
}
TY - JOUR AU - Van Vo AU - Jiawei Luo AU - Bay Vo TI - Time Series Trend Analysis Based on K-Means and Support Vector Machine JO - Computing and Informatics VL - 35 IS - 1 SP - 111 EP - 127 PY - 2016 SN - 1335-9150 UR - https://cai.type.sk/content/2016/1/time-series-trend-analysis-based-on-k-means-and-support-vector-machine/ KW - Machine learning KW - time series trend analysis KW - support vector machines KW - k-means clustering ER -
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. https://cai.type.sk/content/2016/1/time-series-trend-analysis-based-on-k-means-and-support-vector-machine/