Abstract
This paper focuses on the problem of forecasting a classification given a panel data set formed by a (multiple) time series of partitions of a same set of units. As far as we know, in classification and time series analysis this is a completely new problem. A methodology based on a vector autoregressive model is here proposed to directly forecast a partition given a (multiple) time series of partitions with the same fixed number of classes for each time. Two real panel data have been analysed with this new procedure. Open problems are discussed in a final section.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Iezzi, D.F., Vichi, M. (1999). Forecasting a Classification. In: Vichi, M., Opitz, O. (eds) Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60126-2_4
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DOI: https://doi.org/10.1007/978-3-642-60126-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65633-3
Online ISBN: 978-3-642-60126-2
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