Loss of Fitting and Distance Prediction in Fixed vs Updated ARIMA Models

Livio Fenga

Loss of Fitting and Distance Prediction in Fixed vs Updated ARIMA Models

Livio Fenga * §

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§ ISTAT (Italy) - University of California San Diego

Loss of Fitting and Distance Prediction in Fixed vs Updated ARIMA Models

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Abstract

In many cases, it might be advisable to keep an operational time series model fixed for a given span of time, instead of updating it as a new datum becomes available. One common case, is represented by model-based deseasonalization procedures, whose time series models are updated on a regular basis by National Statistical Offices. In fact, in order to minimize the extent of the revisions and grant a greater stability of the already released figures, the interval in between two updating processes is kept “reasonably” long (e.g. one year). Other cases can be found in many contexts, e.g. in engineering for structural reliability analysis or in all those cases where model re-estimation is not a practical or even a viable options, e.g. due to time constraints or computational issues. Clearly, the inevitable trade-off between a fixed models and its updated counterpart, e.g. in terms of fitting performances, out-of-sample prediction capabilities or dynamics explanation should be always accounted for.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Livio Fenga, Livio Fenga. 2017. "Loss of Fitting and Distance Prediction in Fixed vs Updated ARIMA Models". Global Journal of Science Frontier Research, Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 17 (GJSFR Volume 17 Issue F1).

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GJSFR Volume 17 Issue F1

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

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Classification

GJSFR-F Classification MSC 2010: 97K80

Submission ReceivedDecember 27, 2016
Peer Review Double Blind
Handling Editor
Accepted January 25, 2017
Published February 5, 2017

Version of record

v1.2

Issue date

February 26, 2017

Language

English

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Article Matrices

Total Score: 145

Country: Italy

Subject: Global Journal of Science Frontier Research

Authors: Livio Fenga (PhD/Dr. count: 0)

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Publish Date: 2017 02, Sun

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