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- W2914335845 abstract "One of the common goals of time series analysis is to use the observed seriesto inform predictions for future observations. In the absence of any actual newdata to predict, cross-validation can be used to estimate a model's futurepredictive accuracy, for instance, for the purpose of model comparison orselection. As exact cross-validation for Bayesian models is oftencomputationally expensive, approximate cross-validation methods have beendeveloped; most notably methods for leave-one-out cross-validation (LOO-CV). Ifthe actual prediction task is to predict the future given the past, LOO-CVprovides an overly optimistic estimate as the information from futureobservations is available to influence predictions of the past. To tackle theprediction task properly and account for the time series structure, we can useleave-future-out cross-validation (LFO-CV). Like exact LOO-CV, exact LFO-CVrequires refitting the model many times to different subsets of the data. UsingPareto smoothed importance sampling, we propose a method for approximatingexact LFO-CV that drastically reduces the computational costs while alsoproviding informative diagnostics about the quality of the approximation." @default.
- W2914335845 created "2019-02-21" @default.
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- W2914335845 date "2019-02-17" @default.
- W2914335845 modified "2023-09-27" @default.
- W2914335845 title "Approximate leave-future-out cross-validation for time series models" @default.
- W2914335845 hasPublicationYear "2019" @default.
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