学习图结构与不确定性以实现准确且校准良好的时间序列预测
Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting
AI总结：
本文提出STOIC方法，利用时间序列间的随机相关性学习潜在图结构，在多个基准上实现约16%更准确、14%校准更好的预测，并增强对噪声的适应性。
摘要：
多变量时间序列预测是一个重要问题，具有广泛的应用。近期的工作将时间序列之间的关系建模为图，并表明在关系图上传播信息可以改善时间序列预测。然而，在许多情况下，关系信息不可用或存在噪声且不可靠。此外，大多数工作忽略了时间序列在结构学习和推导预测过程中的潜在不确定性，导致结构未能捕捉不确定性，从而使预测分布的不确定性估计较差。我们应对这一挑战并引入了STOIC，它利用时间序列之间的随机相关性来学习时间序列之间的潜在结构，并提供校准良好且准确的预测。在广泛的基准数据集上，STOIC提供了约16%更准确的预测和14%校准更好的预测。STOIC在推理过程中还表现出对数据噪声更好的适应性，并在各种基准中捕捉到重要且有用的关系信息。
英文摘要：
Multi-variate time series forecasting is an important problem with a wide range of applications. Recent works model the relations between time-series as graphs and have shown that propagating information over the relation graph can improve time series forecasting. However, in many cases, relational information is not available or is noisy and reliable. Moreover, most works ignore the underlying uncertainty of time-series both for structure learning and deriving the forecasts resulting in the structure not capturing the uncertainty resulting in forecast distributions with poor uncertainty estimates. We tackle this challenge and introduce STOIC, that leverages stochastic correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides around 16% more accurate and 14% better-calibrated forecasts. STOIC also shows better adaptation to noise in data during inference and captures important and useful relational information in various benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
