Next Article in Journal
Discrete Wavelet Packet Transform-Based Industrial Digital Wireless Communication Systems
Previous Article in Journal
Related Stocks Selection with Data Collaboration Using Text Mining
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(3), 103;

A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
College of Information Science and Technology, Hainan University, Haikou 570228, China
Author to whom correspondence should be addressed.
Received: 17 December 2018 / Revised: 28 February 2019 / Accepted: 1 March 2019 / Published: 7 March 2019
Full-Text   |   PDF [6398 KB, uploaded 7 March 2019]   |  


Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes. View Full-Text
Keywords: financial time series; parameter optimization; DBSCAN; SVR financial time series; parameter optimization; DBSCAN; SVR

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Huang, M.; Bao, Q.; Zhang, Y.; Feng, W. A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR. Information 2019, 10, 103.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top