Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X, which is a time series, and an output Y = f(X) where f is a very complicated function, whose computational cost for every new input is very high. We are given two sets of observations of X, S1 and S2 of different sizes such that only f(S1) isavailable. We tackle the problem of selecting a subsample S3 ∈ S2 of a smaller size on which to run the complex model f and such that distribution of f(S3) is close to that of f(S1). We adapt to this new framework five algorithms introduced in a previous work "Subsampling under Distributional Constraints" to solve this problem and show their efficiency using time series data.

Este artículo forma parte de las actas del "The 8th International Conference on Time Series and Forecasting."