Discovery of significant intervals in time-series data

• Autorzy: Srinivasan A. , Chakravarthy S. , Sheshta S.
• Konferencja: ADBIS Workshop on Data Mining and Knowledge Discovery (ADMKD'2005) / sympozjum [1st; September 15-16, 2005; Tallinn, Estonia]
• Języki publikacji: EN

Abstrakty

EN

This paper deals with time-series sensor data to discover significant intervals for prediction. In our project on intelligent home environment, we need to predict agent's actions (tum on/off the appliances) using previously collected sensor data. We propose an approach that replaces the time-series point values with time-series intervals, which represent the characteristics of the data. Time-series data is folded over a periodicity (day, week, etc.) to form intervals and significant intervals are discovered from them that satisfy minimum support and maximum interval-length criteria. By compressing the time-series data and working with intervals, discovery of significant intervals (for prediction) is efficient. Also, sequential mining algorithms perform better if they are modified to work with reduced dataset of significant intervals. In this paper, we present a suite of algorithms for detecting significant intervals, discuss their characteristics, advantages and disadvantages and analyze their performance.

Słowa kluczowe

EN

data mining; significant interval discovery; time-series data; data compression

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2006
• Tom: Vol. 31, No. 1
• Strony: 87--102
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Srinivasan A.

autor

Chakravarthy S.

autor

Sheshta S.

• CSE Department and IT Laboratory, The University of Texas at Arlington,

Bibliografia

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