title: On estimating the reliability of ecological forecasts.
abstract: Recent work has highlighted the utility of nonparametric forecasting methods for predicting ecological time series (Perretti et al., 2013. Proc. Natl. Acad. Sci. U.S.A. 110, 5253-5257). However, one topic that has received considerably less attention is the quantification of uncertainty in nonparametric forecasts. This important topic was brought to the forefront in the recent work by Jabot (2014. J. Theor. Biol.). Here, we add to this emerging discussion by reviewing the available methods for quantifying forecast uncertainty in nonparametric models. We conclude with a demonstration of one such method using the simulation model of Jabot (2014. J. Theor. Biol.). We find that nonparametric forecast error is accurately estimated with as few as 10 observations in the time series.
authors.affiliation: Duke University, Durham, NC 27708, USA; National Marine Fisheries Service, NOAA, Woods Hole, MA 02543, USA. Electronic address: charles.perretti@noaa.gov.
authors.forename: Charles T
authors.initials: CT
authors.lastname: Perretti
authors.affiliation: National Marine Fisheries Service, NOAA, Santa Cruz, CA 95060, USA.
authors.forename: Stephan B
authors.initials: SB
authors.lastname: Munch
mesh_terms: D004463:Ecology; D017711:Nonlinear Dynamics
keywords: Deterministic chaos; Model checking; Nonlinear dynamics; Nonparametric forecasting; Observation error; Time series analysis