—Similarity metric is of fundamental importance
for similarity matching and subsequence query in time series
applications. Most existing approaches measure the similarity
by calculating and aggregating the point-to-point distance, few
of them take the segment trend duration into account. In this
paper, upon analyzing the properties of financial time series, we
define a time series notation which is more intuitive and
expressive. Base on that, a new similarity model is proposed.
Experiments on both real foreign currency exchange rate data
and stock market data are performed. The result shows the
effectiveness and good accuracy of our method. The similarity
model is also proved to be segmentation algorithm independent
thus can be combined with other segmentations for similarity
query, pattern matching, classification, and clustering.
—financial data, piecewise linear representation,
radian distance, segment duration, similarity, time series.
Yongwei Ding is with the College of Computer Science and Technology,
Zhejiang University, Hangzhou, Zhejiang 310027 P.R. China (phone:
86-135-8803-7210, e-mail: email@example.com).
Xiaohu Yang is with the College of Computer Science and Technology,
Zhejiang University, Hangzhou, Zhejiang 310027 P.R. China (e-mail:
Alexsander J. Kavs is with the State Street Corporation, Boston, MA
02111 USA (e-mail: firstname.lastname@example.org)
Juefeng Li is with the State Street Technology (Zhejiang), Hangzhou,
Zhejiang 310030 P.R. China (e-mail: email@example.com).
Cite:Yongwei Ding, Xiaohu Yang, Alexsander J. Kavs, and Juefeng Li, "Financial Data Representation and Similarity Model," International Journal of Trade, Economics and Finance vol.1, no.4, pp. 320-324, 2010.