{"refrec":{"BRefID":339069,"RR":"<b>Okamura, H.; Osada, Y.; Nishijima, S.; Eguchi, S.</b> (2021). Novel robust time series analysis for long-term and short-term prediction. <i>NPG Scientific Reports 11(1)</i>: 11938. <a href=\"https://dx.doi.org/10.1038/s41598-021-91327-8\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-021-91327-8</a>","BEntID":335704,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 11(1)</i>: 11938. <a href=\"https://dx.doi.org/10.1038/s41598-021-91327-8\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-021-91327-8</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Okamura, H.; Osada, Y.; Nishijima, S.; Eguchi, S.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Okamura, H. <i>et al.</i>","Englishabstract":"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.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Novel robust time series analysis for long-term and short-term prediction","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-12 01:32:04.281091","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2021-06-16","DateCreate":"2021-06-16","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000662871900043","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-021-91327-8"},"refs":null,"anarec":{"AnaID":339069,"PubliDate":2021,"Pagination":"11938","XtraPublOfAnaID":null,"ISBN":null,"Volume":"11","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). 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