Abstract
A new method for estimating ARIMA model parameters of nonstationary time series with missing observations is considered. Two approaches are proposed: (i) a nonstationary time series is transformed into stationary one, a stationary time series missing data iterative estimation algorithm is applied and at its end the inverse transformation is performed to obtain the estimates of original time series missing observations, (ii) the inverse transformation and the calculation o original time series missing observations is performed in each loop of the iterative procedure and not only at the end. Use of the methods is illustrated by a case study.
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Literature
Box G.E.P. and Jenkins G.M., Time Series Analysis: Forecasting and Control. Holden Day, San Francisco 1970.
Čepar D., Radalj Z., ARMA Models of Time Series with Missing Data. Proceedings of the Fifth European Conference on Mathematics in Industry, ECMI 7, p. 189–192, B. G. Teubner Stuttgart and Kluwer Academic Press 1991.
McLeod G., Box Jenkins in Practice. Gwilym Jenkins & Partners Ltd. 1982.
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© 1992 Springer Fachmedien Wiesbaden
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Čepar, D., Radalj, Z., Vovk, B. (1992). Estimating Nonstationary Time Series with Missing Data. In: Hodnett, F. (eds) Proceedings of the Sixth European Conference on Mathematics in Industry August 27–31, 1991 Limerick. European Consortium for Mathematics in Industry. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-663-09834-8_16
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DOI: https://doi.org/10.1007/978-3-663-09834-8_16
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-663-09835-5
Online ISBN: 978-3-663-09834-8
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