Please use this identifier to cite or link to this item: https://doi.org/10.1214/10-STS345
DC FieldValue
dc.titleFeature matching in time series modeling
dc.contributor.authorXia, Y.
dc.contributor.authorTong, H.
dc.date.accessioned2014-10-28T05:12:13Z
dc.date.available2014-10-28T05:12:13Z
dc.date.issued2011
dc.identifier.citationXia, Y., Tong, H. (2011). Feature matching in time series modeling. Statistical Science 26 (1) : 21-46. ScholarBank@NUS Repository. https://doi.org/10.1214/10-STS345
dc.identifier.issn08834237
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105150
dc.description.abstractUsing a time series model to mimic an observed time series has a long history. However, with regard to this objective, conventional estimation methods for discrete-time dynamical models are frequently found to be wanting. In fact, they are characteristically misguided in at least two respects: (i) assuming that there is a true model; (ii) evaluating the efficacy of the estimation as if the postulated model is true. There are numerous examples of models, when fitted by conventional methods, that fail to capture some of the most basic global features of the data, such as cycles with good matching periods, singularities of spectral density functions (especially at the origin) and others. We argue that the shortcomings need not always be due to the model formulation but the inadequacy of the conventional fitting methods. After all, all models are wrong, but some are useful if they are fitted properly. The practical issue becomes one of how to best fit the model to data. Thus, in the absence of a true model, we prefer an alternative approach to conventional model fitting that typically involves one-step-ahead prediction errors. Our primary aim is to match the joint probability distribution of the observable time series, including long-term features of the dynamics that underpin the data, such as cycles, long memory and others, rather than shortterm prediction. For want of a better name, we call this specific aim feature matching. The challenges of model misspecification, measurement errors and the scarcity of data are forever present in real time series modeling. In this paper, by synthesizing earlier attempts into an extended-likelihood, we develop a systematic approach to empirical time series analysis to address these challenges and to aim at achieving better feature matching. Rigorous proofs are included but relegated to the Appendix. Numerical results, based on both simulations and real data, suggest that the proposed catch-all approach has several advantages over the conventional methods, especially when the time series is short or with strong cyclical fluctuations. We conclude with listing directions that require further development.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1214/10-STS345
dc.sourceScopus
dc.subjectACF
dc.subjectBayesian statistics
dc.subjectBlack-box models
dc.subjectBlowflies
dc.subjectBox's dictum
dc.subjectCalibration
dc.subjectCatch-all approach
dc.subjectData mining
dc.subjectEcological populations
dc.subjectEpidemiology
dc.subjectFeature consistency
dc.subjectFeature matching
dc.subjectLeast squares estimation
dc.subjectMaximum likelihood
dc.subjectMeasles
dc.subjectMeasurement errors
dc.subjectMisspecified models
dc.subjectModel averaging
dc.subjectMulti-step-ahead prediction
dc.subjectNonlinear time series
dc.subjectObservation errors
dc.subjectOptimal parameter
dc.subjectPeriodicity
dc.subjectPopulation models
dc.subjectSea levels
dc.subjectShort time series
dc.subjectSIR epidemiological model
dc.subjectSkeleton
dc.subjectSubstantive models
dc.subjectSunspots
dc.subjectThreshold autoregressive models,Whittle's likelihood
dc.subjectXT-likelihood
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1214/10-STS345
dc.description.sourcetitleStatistical Science
dc.description.volume26
dc.description.issue1
dc.description.page21-46
dc.identifier.isiut000292424900007
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