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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 1/2015

01.03.2015 | Regular Article

Modeling and forecasting interval time series with threshold models

verfasst von: Paulo M. M. Rodrigues, Nazarii Salish

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2015

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Abstract

This paper proposes threshold models to analyze and forecast interval-valued time series. A relatively simple algorithm is proposed to obtain least square estimates of the threshold and slope parameters. The construction of forecasts based on the proposed model and methods for the analysis of their forecast performance are also introduced and discussed, as well as forecasting procedures based on the combination of different models. To illustrate the usefulness of the proposed methods, an empirical application on a weekly sample of S&P500 index returns is provided. The results obtained are encouraging and compare very favorably to available procedures.
Vorheriger Artikel Trimmed fuzzy clustering for interval-valued data
Nächster Artikel Principal component analysis for probabilistic symbolic data: a more generic and accurate algorithm

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Fußnoten
1
Note that it is not clear if the results obtained in the following sections will hold for the lower-upper bound representation of ITS, and we do not pursue this extension in this paper. However, this presents an interesting line of research for further investigation.
 
2
Following the suggestion of one referee, to compare predictive accuracy we have also conducted Diebold–Mariano tests (see Appendix) and the results obtained corroborate these conclusions.
 
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Metadaten
Titel
Modeling and forecasting interval time series with threshold models
verfasst von
Paulo M. M. Rodrigues
Nazarii Salish
Publikationsdatum
01.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 1/2015
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-014-0170-x

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