Stilt: Easy Emulation of Time Series AR(1) Computer Model Output in Multidimensional Parameter Space
In one sentence. R package stilt provides simple AR(1) Gaussian process emulation of time series climate model output, with cross-validation and no MCMC needed.
Abstract
Statistically approximating or "emulating" time series model output in parameter space is a common problem in climate science and other fields.There are many packages for spatio-temporal modeling.However, they often lack focus on time series, and exhibit statistical complexity.Here, we present the R package stilt designed for simplified AR(1) time series Gaussian process emulation, and provide examples relevant to climate modelling.Notably absent is Markov chain Monte Carlo estimation -a challenging concept to many scientists.We keep the number of user choices to a minimum.Hence, the package can be useful pedagogically, while still applicable to real life emulation problems.We provide functions for emulator cross-validation, empirical coverage, prediction, as well as response surface plotting.While the examples focus on climate model emulation, the emulator is general and can be also used for kriging spatio-temporal data.
Labels
- Topic
- Climate emulation
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.32614/rj-2018-049 OpenAlex:W2909941368
- Added
- 2026-10-22
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