Abstract
It is desired to extract important information on-line from high-dimensional time series because of difficulty in reflecting the data fully in decision making. In econometric techniques, previous works primarily focus on prediction of price. To make decision in business practice, it is important to focus on human-machine interaction based on chance discovery. Here we propose Non-Conformity Detection as a method for aiding to humans to discover chances. Non-Conformity Detection is designed to detect a noteworthy point that behaves exceptionally compared to surrounding points in time series. In the experiment, the method of Non-Conformity Detection is applied to the time series of 29 stocks return in the electrical machine industry. As the result, four dates among the detected top five non-conformity points coincide with the important dates that professional analysts judged for making investment decision. These results suggest Non-Conformity Detection support the discovery of chances for decision making.
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This research was partially supported by JST-CREST.
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Kasuga, A., Ohsawa, Y., Yoshino, T., Ashida, S. (2016). Non-Conformity Detection in High-Dimensional Time Series of Stock Market Data. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_60
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DOI: https://doi.org/10.1007/978-3-319-42007-3_60
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