# Pattern discovery from stock time series using self-organizing maps

@inproceedings{Fu2016PatternDF, title={Pattern discovery from stock time series using self-organizing maps}, author={Tak-Chung Fu and F. Chung and R. Luk and Ng}, year={2016} }

† This work was supported by the RGC CERG project PolyU 5065/98E and the Departmental Grant H-ZJ84 ‡ Corresponding author ABSTRACT Pattern discovery from time series is of fundamental importance. Particularly when the domain expert derived patterns do not exist or are not complete, an algorithm to discover specific patterns or shapes automatically from the time series data is necessary. Such an algorithm is noteworthy in that it does not assume prior knowledge of the number of interesting… Expand

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