Approximate leave-future-out cross-validation for Bayesian time series models. Issue 14 (21st September 2020)

• Record Type: Journal Article
• Authors: Bürkner, Paul-Christian; Gabry, Jonah; Vehtari, Aki
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 14(2020)
• Page Start: 2499
• Page End: 2523
• Publication Date: 2020-09-21
• Subjects: Time series analysis -- cross-Validation -- Bayesian inference -- pareto Smoothed importance sampling; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1783262
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : One of the common goals of time series analysis is to use the observed series to inform predictions for future observations. In the absence of any actual new data to predict, cross-validation can be used to estimate a model's future predictive accuracy, for instance, for the purpose of model comparison or selection. Exact cross-validation for Bayesian models is often computationally expensive, but approximate cross-validation methods have been developed, most notably methods for leave-one-out cross-validation (LOO-CV). If the actual prediction task is to predict the future given the past, LOO-CV provides an overly optimistic estimate because the information from future observations is available to influence predictions of the past. To properly account for the time series structure, we can use leave-future-out cross-validation (LFO-CV). Like exact LOO-CV, exact LFO-CV requires refitting the model many times to different subsets of the data. Using Pareto smoothed importance sampling, we propose a method for approximating exact LFO-CV that drastically reduces the computational costs while also providing informative diagnostics about the quality of the approximation.