Abstract
Graph-based data has played an important role in representing complex patterns from real-world data, but there is very little work on mining time series with graphs. And those existing graph-based time series mining methods always use well-selected data. In this paper, we investigate a method for extracting graph structures, which contain the structural information that cannot be captured by vector-based data, from the whole Chinese financial time series. We call them time-varying networks, each node in these networks represents the individual time series of a stock and each undirected edge between two nodes represents the correlation between two stocks. We further review a linear-time graph kernel for labeled graphs and show whether the graph kernel, together with time-varying networks, can be used to analyze Chinese financial time series. In the experiments, we apply our method to analyze the whole Chinese Stock Market daily transaction data, i.e., the stock prices data, and use the graph kernel to measure similarities between those extracted networks. Then we compare the performances of our method and other sequence-based or vector-based methods by using kernel principle components analysis to map those results into low dimensional feature space. The experimental results demonstrate the efficiency and effectiveness of our methods together with graph kernels in analyzing Chinese financial time series.
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References
Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Neural Information Processing Systems, pp. 1025–1035 (2017)
Li, X., et al.: Visual tracking via random walks on graph model. IEEE Trans. Cybern. 46(9), 2144–2155 (2016)
Wu, J., et al.: Boosting for multi-graph classification. IEEE Trans. Cybern. 45(3), 416–429 (2015)
Kashima, H.: Marginalized kernels between labeled graphs. In: Proceedings of the Twentieth International Conference on Machine Learning, pp. 321–328 (2003)
Vishwanathan, S.V.N., et al.: Graph kernels. J. Mach. Learn. Res. 11(2), 1201–1242 (2008)
Bai, L., et al.: An aligned subtree kernel for weighted graphs. In: International Conference on Machine Learning, pp. 30–39 (2015)
Haussler, D.: Convolution kernels on discrete structures. Technical report, vol. 7, pp. 95–114 (1999)
Bai, L., et al.: Quantum kernels for unattributed graphs using discrete-time quantum walks. Pattern Recognit. Lett. 87(C), 96–103 (2016)
Gärtner, T., Lloyd, J.W., Flach, P.A.: Kernels and distances for structured data. Mach. Learn. 57(3), 205–232 (2004)
Bai, L., Hancock, E.R.: Fast depth-based subgraph kernels for unattributed graphs. Pattern Recognit. 50(C), 233–245 (2016)
Bonanno, G., et al.: Networks of equities in financial markets. Eur. Phys. J. B 38(2), 363–371 (2004)
Eisenberg, L., Noe, T.H.: Systemic risk in financial networks. SSRN Electron. J. (2007)
Bai, L., Escolano, F., Hancock, E.R.: Depth-based hypergraph complexity traces from directed line graphs. Elsevier Science Inc. (2016)
Bai, L., et al.: A quantum Jensen-Shannon graph kernel for unattributed graphs. Pattern Recognit. 48(2), 344–355 (2015)
Hido, S., Kashima, H.: A linear-time graph kernel. In: Ninth IEEE International Conference on Data Mining, pp. 179–188. IEEE Computer Society (2009)
Prim, R.C.: Shortest connection networks and some generalizations. Bell Labs Tech. J. 36(6), 1389–1401 (2013)
Seidel, R.: On the all-pairs-shortest-path problem. J. Comput. Syst. Sci. 51(3), 400–403 (1995)
Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics 27(4), 857–871 (1971)
Cuturi, M.: Fast global alignment kernels. In: International Conference on Machine Learning, pp. 929–936 (2011)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(10), 2825–2830 (2012)
Schölkopf, B., Smola, A., Mller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant no. 61602535, 61503422 and 61773415), the Open Projects Program of National Laboratory of Pattern Recognition, and the program for innovation research in Central University of Finance and Economics.
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Jiao, Y., Cui, L., Bai, L., Wang, Y. (2018). Analyzing Time Series from Chinese Financial Market Using a Linear-Time Graph Kernel. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_22
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