Novel robust time series analysis for long-term and short-term prediction

Abstract Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whe...

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Autores principales:	Hiroshi Okamura, Yutaka Osada, Shota Nishijima, Shinto Eguchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/894b93dc70be47c9b2221d800da7711f
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spelling	oai:doaj.org-article:894b93dc70be47c9b2221d800da7711f2021-12-02T17:52:12ZNovel robust time series analysis for long-term and short-term prediction10.1038/s41598-021-91327-82045-2322https://doaj.org/article/894b93dc70be47c9b2221d800da7711f2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91327-8https://doaj.org/toc/2045-2322Abstract Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.Hiroshi OkamuraYutaka OsadaShota NishijimaShinto EguchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hiroshi Okamura Yutaka Osada Shota Nishijima Shinto Eguchi Novel robust time series analysis for long-term and short-term prediction
description	Abstract Nonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.
format	article
author	Hiroshi Okamura Yutaka Osada Shota Nishijima Shinto Eguchi
author_facet	Hiroshi Okamura Yutaka Osada Shota Nishijima Shinto Eguchi
author_sort	Hiroshi Okamura
title	Novel robust time series analysis for long-term and short-term prediction
title_short	Novel robust time series analysis for long-term and short-term prediction
title_full	Novel robust time series analysis for long-term and short-term prediction
title_fullStr	Novel robust time series analysis for long-term and short-term prediction
title_full_unstemmed	Novel robust time series analysis for long-term and short-term prediction
title_sort	novel robust time series analysis for long-term and short-term prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/894b93dc70be47c9b2221d800da7711f
work_keys_str_mv	AT hiroshiokamura novelrobusttimeseriesanalysisforlongtermandshorttermprediction AT yutakaosada novelrobusttimeseriesanalysisforlongtermandshorttermprediction AT shotanishijima novelrobusttimeseriesanalysisforlongtermandshorttermprediction AT shintoeguchi novelrobusttimeseriesanalysisforlongtermandshorttermprediction
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Novel robust time series analysis for long-term and short-term prediction

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