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doi:10.1016/j.ejor.2005.03.049 
Copyright © 2005 Elsevier B.V. All rights reserved.
Stochastics and Statistics
Seasonal clustering technique for time series data
Tasha R. Inniss
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Received 24 June 2004;
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Abstract
In data mining, the unsupervised learning technique of clustering is a useful method for ascertaining trends and patterns in data. Most general clustering techniques do not take into consideration the time-order of data. In this paper, mathematical programming and statistical techniques and methodologies are combined to develop a seasonal clustering technique for determining clusters of time series data. We apply this technique to weather and aviation data to determine probabilistic distributions of arrival capacity scenarios, which can be used for efficient traffic flow management. In general, this technique may be used for seasonal forecasting and planning.
Keywords: Partitional clustering; Seasonal clustering; Set partitioning integer program; Empirical distribution function; Mean square ratio
Article Outline
- 1. Motivation for development of technique
- 2. Overview of clustering
- 3. Modeling seasonal clustering problem
- 4. Seasonal clustering approach
- 4.1. Determining seasonal clusters using GDP data
- 4.2. Determining seasonal clusters using fog weather data
- 5. Evaluating sets of seasons (computational results)
- 6. Conclusion
- References
