  This paper presents non-parametric baseline models for time series
forecasting. Unlike classical forecasting models, the proposed approach does
not assume any parametric form for the predictive distribution and instead
generates predictions by sampling from the empirical distribution according to
a tunable strategy. By virtue of this, the model is always able to produce
reasonable forecasts (i.e., predictions within the observed data range) without
fail unlike classical models that suffer from numerical stability on some data
distributions. Moreover, we develop a global version of the proposed method
that automatically learns the sampling strategy by exploiting the information
across multiple related time series. The empirical evaluation shows that the
proposed methods have reasonable and consistent performance across all
datasets, proving them to be strong baselines to be considered in one's
forecasting toolbox.
