Perception Based Patterns in Time Series
Data Mining
I. Batyrshin, L. Sheremetov, and R. Herrera-Avelar
Summary. Import of intelligent features to systems supporting human decisions in
problems related with analysis of time series data bases is a promising research field.
Such systems should be able to operate with fuzzy perception-based information about
time moments and time intervals; about time series values, trends and shapes; about
associations between time series and time series patterns, etc., to formalize human
knowledge, to simulate human reasoning and to reply on human questions. The
chapter discusses methods developed in TSDM to describe linguistic perception-based
patterns in time series databases. The survey considers different approaches to
description of such patterns which use sign of derivatives, scaling of trends and
shapes, linguistic interpretation of patterns obtained as result of clustering, a grammar
for generation of complex patterns from shape primitives, and temporal relations
between patterns. These descriptions can be extended by using fuzzy granulation of
time series patterns to make them more adequate to perceptions used in human
reasoning. Several approaches to relate linguistic descriptions of experts with auto-
matically generated texts of summaries and linguistic forecasts are considered. Finally,
we discuss the role of perception-based time series data mining and computing with
words and perceptions in construction of intelligent systems that use expert knowledge
and decision making procedures in time series data base domains.
1 Introduction
Till now most of the decision making procedures in problems related with time
series (TS) analysis in economics and finance are based on human decisions
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Intelligence (SCI) 36, 85–118 (2007)
I. Batyrshin et al.: Perception Based Patterns in Time Series Data Mining, Studies in Computational