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Discovery of Temporal Patterns - Learning Rules about the Qualitative Behaviour of Time Series (2001)
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BibTeX
@MISC{Höppner01discoveryof,
author = {Frank Höppner},
title = {Discovery of Temporal Patterns - Learning Rules about the Qualitative Behaviour of Time Series},
year = {2001}
}
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Abstract
. Recently, association rule mining has been generalized to the discovery of episodes in event sequences. In this paper, we additionally take durations into account and thus present a generalization to time intervals. We discover frequent temporal patterns in a single series of such labeled intervals, which we call a state sequence. A temporal pattern is dened as a set of states together with their interval relationships described in terms of Allen's interval logic, for instance \A before B, A overlaps C, C overlaps B" or equivalently \state A ends before state B starts, the gap is covered by state C". As an example we consider the problem of deriving local weather forecasting rules that allow us to conclude from the qualitative behaviour of the air-pressure curve to the wind-strength. Here, the states have been extracted automatically from (multivariate) time series and characterize the trend of the time series locally within the assigned time interval. 1
Keyphrases
time series qualitative behaviour temporal pattern learning rule labeled interval time interval single series association rule mining event sequence air-pressure curve state sequence temporal pattern interval relationship assigned time interval frequent temporal pattern local weather forecasting rule interval logic
