论文标题
节：非平稳时间序列中概率推断的非线性状态空间模型
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series
论文作者
论文摘要
具有长期结构的时间序列是在各种情况下出现的，并且捕获这种时间结构是推理和预测设置的时间序列分析的关键挑战。传统上，国家空间模型已成功地提供了潜在空间中轨迹的不确定性估计。最近，深度学习，基于注意力的方法已经实现了序列建模的最先进的性能，尽管通常需要大量的数据和参数才能做到这一点。我们建议STANZA是一种非线性，非平稳状态空间模型，作为一种中间方法，以填补传统模型与复杂时间序列的现代深度学习方法之间的空白。 Stanza在竞争性预测准确性与高度结构化时间序列的概率，可解释的推断之间取得了平衡。特别是，STANZA在现实数据集中与深度LSTMS实现了预测准确性竞争，尤其是用于预测的多步骤。
Time series with long-term structure arise in a variety of contexts and capturing this temporal structure is a critical challenge in time series analysis for both inference and forecasting settings. Traditionally, state space models have been successful in providing uncertainty estimates of trajectories in the latent space. More recently, deep learning, attention-based approaches have achieved state of the art performance for sequence modeling, though often require large amounts of data and parameters to do so. We propose Stanza, a nonlinear, non-stationary state space model as an intermediate approach to fill the gap between traditional models and modern deep learning approaches for complex time series. Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series. In particular, Stanza achieves forecasting accuracy competitive with deep LSTMs on real-world datasets, especially for multi-step ahead forecasting.
