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- W2502181153 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." @default.
- W2502181153 created "2016-08-23" @default.
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- W2502181153 date "2016-01-01" @default.
- W2502181153 modified "2023-09-30" @default.
- W2502181153 title "Non-Conformity Detection in High-Dimensional Time Series of Stock Market Data" @default.
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- W2502181153 doi "https://doi.org/10.1007/978-3-319-42007-3_60" @default.
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