通过引入隐变量表示，提升时间序列预测的准确性与鲁棒性。
Deconfounded Time Series Forecasting: A Causal Inference Approach
- 从历史数据中学习隐变量表示并融入预测过程
- 在气候科学数据上显著优于不考虑混杂因素的传统方法
- 适合需要高可靠性预测的领域如气候建模
时间序列预测在多个领域至关重要，准确的预测可支持科学决策。传统方法通常仅依赖当前变量观测来预测未来结果，却忽视了同时影响预测变量和目标变量的潜在混杂因素，导致模型偏差并降低性能。本文提出一种增强型预测方法，通过历史数据学习潜在混杂因子的表示，并将其整合进预测流程，以提升预测的准确性和鲁棒性。该方法在气候科学数据上的应用表明，其性能显著优于未考虑混杂因素的传统方法。
原文摘要 · Abstract (English)
Time series forecasting is a critical task in various domains, where accurate predictions can drive informed decision-making. Traditional forecasting methods often rely on current observations of variables to predict future outcomes, typically overlooking the influence of latent confounders, unobserved variables that simultaneously affect both the predictors and the target outcomes. This oversight can introduce bias and degrade the performance of predictive models. In this study, we address this challenge by proposing an enhanced forecasting approach that incorporates representations of latent confounders derived from historical data. By integrating these confounders into the predictive process, our method aims to improve the accuracy and robustness of time series forecasts. The proposed approach is demonstrated through its application to climate science data, showing significant improvements over traditional methods that do not account for confounders.
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