An FDA-based Stock Exchange Price Curve Feature Recognition and Analysis Method

DOI

10.2991/ifmeita-16.2016.142How to use a DOI?

Keywords

functional data analysis, curve feature extraction, K-means clustering, curve classification

Abstract

The classification and trend prediction of stock exchange price via trading history becomes the crucial part of intelligent stock analysis software nowadays. To figure out the variation pattern of stock price better, the curve-based method is proved to be efficient when applied to large discrete dataset. This paper proposed a FDA-based stock price curve recognition method in order to provide support for stock price prediction. On the basis of fitting function, extract segmented variation trend, segmented variation rate and segmented Root Mean Square as features which reflect the information of curve shape. And give weight to the three features to form the feature vector of the curve. Then conduct K-means clustering on these feature vectors. The result is the same to the subjective classification, so that in this way obtaining the class label of each curve. Finally classify the unknown curve with neural network. On the test set, the correct recognition rate reaches 80%.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yan Xue
AU  - Tong Ge
AU  - Hongxia Bie
PY  - 2016/01
DA  - 2016/01
TI  - An FDA-based Stock Exchange Price Curve Feature Recognition and Analysis Method
BT  - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SP  - 775
EP  - 780
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.142
DO  - 10.2991/ifmeita-16.2016.142
ID  - Xue2016/01
ER  -