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Proceedings Paper

Wavelet-based time series prediction for air traffic data
Author(s): Ilona Weinreich; Heike Rickert; Michael Lukaschewitsch
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Paper Abstract

We study analysis and forecasting strategies for time series based on multiscale analysis. The method is illustrated for a set of data collecting several years of booking information from the air traffic company Lufthansa Systems GmbH, Berlin. In particular, we deal with data where the variability of the forecast units leads to different problems in computing. We consider several years of subsequent data and apply a wavelet decomposition over a certain number of scales. In wavelet domain the data are subdivided in low and high frequency parts. Forecast values on each scale are calculated, the inverse wavelet transform yields a forecast for the whole signal. In the present paper we describe the analysis of several historical booking data sets from Lufthansa Systems GmbH dealing with data over a period of 4 years. Based on the wavelet transform we apply a forecast to the data. The forecast itself depends on the behaviour of the data on each scale. The wavelet decomposition can be used to reveal trends and seasonal influences.

Paper Details

Date Published: 27 February 2004
PDF: 11 pages
Proc. SPIE 5266, Wavelet Applications in Industrial Processing, (27 February 2004); doi: 10.1117/12.516075
Show Author Affiliations
Ilona Weinreich, Fachhocschule Koblenz (Germany)
Heike Rickert, Lufthansa Systems GmbH (Germany)
Michael Lukaschewitsch, Lufthansa Systems GmbH (Germany)

Published in SPIE Proceedings Vol. 5266:
Wavelet Applications in Industrial Processing
Frederic Truchetet, Editor(s)

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