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Information (Switzerland), volume 10, issue 3, pages 103

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

Publication typeJournal Article
Publication date2019-03-07
Q2
Q3
SJR0.703
CiteScore6.9
Impact factor2.4
ISSN20782489
Information Systems
Abstract

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.

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GOST |
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GOST Copy
Huang M. et al. A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR // Information (Switzerland). 2019. Vol. 10. No. 3. p. 103.
GOST all authors (up to 50) Copy
Huang M., Bao Q., Zhang Yu., Feng W. A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR // Information (Switzerland). 2019. Vol. 10. No. 3. p. 103.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.3390/info10030103
UR - https://doi.org/10.3390/info10030103
TI - A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR
T2 - Information (Switzerland)
AU - Huang, Mengxing
AU - Bao, Qili
AU - Zhang, Yu
AU - Feng, Wenlong
PY - 2019
DA - 2019/03/07
PB - MDPI
SP - 103
IS - 3
VL - 10
SN - 2078-2489
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2019_Huang,
author = {Mengxing Huang and Qili Bao and Yu Zhang and Wenlong Feng},
title = {A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR},
journal = {Information (Switzerland)},
year = {2019},
volume = {10},
publisher = {MDPI},
month = {mar},
url = {https://doi.org/10.3390/info10030103},
number = {3},
pages = {103},
doi = {10.3390/info10030103}
}
MLA
Cite this
MLA Copy
Huang, Mengxing, et al. “A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR.” Information (Switzerland), vol. 10, no. 3, Mar. 2019, p. 103. https://doi.org/10.3390/info10030103.
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