Clustering Of Time Series Using A Similarity
Between Segments And Bands Determined By
Patterns Of Technical Analysis
Author(s)
R. Basagoiti & E. Juaristi
Abstract
The representation based on segments, continuous or discontinuous, has been
used for dimensionality reduction of temporal data. This reduction is essential
for the posterior process of data mining. In this work, patterns of technical
analysis, like those defined in Lo et al. (2002), are used to extract the extremes to
get two different representations. The first one is based on segments drawn
between extremes of the patterns, STA. The second one is based on the best
harmonic of the Fourier decomposition between the same extremes, FTA. Due to
the subjective nature of technical analysis some parameters are considered in the
process of extreme extraction and pattern selection. Once a representation is
adopted, similarities between segments defined in Keogh et al. (2002) and
between bands suggested by us are used for clustering and the results compared
with those obtained with the Euclidean and the lower bound of the dynamic time
warping distance defined in Keogh.
Keywords: data approximation, dimensionality reduction, time series clustering,
pattern extraction.
1 Introduction
With the rapid increase of stored data, the interest in the discovery of hidden
information has exploded in the last decade. The focus has mainly been on
classification, clustering, query by content and relationship finding. Treat data
with temporal dependencies is an important problem. A time series data is a
sequence of real values, each of which represents a value measured at a point in
time. We can find examples of time series data in diverse sources and
applications, such as stock prices and currency exchange data. There has been
Keywords
data approximation, dimensionality reduction, time series clustering,
pattern extraction.
Related Book
Other papers in this volume
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