论文标题
基于扩散的时间序列插补和通过结构化状态空间模型进行预测
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
论文作者
论文摘要
缺失值的插补代表了许多实际数据分析管道的重要障碍。在这里，我们专注于时间序列数据，并提出了SSSD，这是一种依赖两种新兴技术的归纳模型，即（条件）扩散模型作为最先进的生成模型和结构化状态空间模型作为内部模型体系结构，这些模型特别适合于时间序列数据中捕获长期依赖性。我们证明，在广泛的数据集和不同的丢失方案（包括具有挑战性的停电失误的情况下），SSSD匹配甚至超过了最先进的概率插补和预测性能，在这些情况下，先前的方法未能提供有意义的结果。
The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputation model that relies on two emerging technologies, (conditional) diffusion models as state-of-the-art generative models and structured state space models as internal model architecture, which are particularly suited to capture long-term dependencies in time series data. We demonstrate that SSSD matches or even exceeds state-of-the-art probabilistic imputation and forecasting performance on a broad range of data sets and different missingness scenarios, including the challenging blackout-missing scenarios, where prior approaches failed to provide meaningful results.
