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.

翻译：本文提出用于时间序列预测的非参数基线模型。与经典预测模型不同，本方法不假设预测分布具有任何参数形式，而是通过根据可调策略从经验分布中采样来生成预测。这一特性使得模型总能生成合理预测（即预测值落在观测数据范围内），而不会像经典模型那样因某些数据分布的数值稳定性问题而失效。此外，我们开发了所提方法的全局版本，通过利用多个相关时间序列间的信息自动学习采样策略。实验评估表明，所提方法在所有数据集上均展现出合理且一致的性能，验证了其作为预测工具箱中值得考虑的强基线模型的有效性。