Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy

Jiajie Wang, Zhiyuan Jerry Lin, Wen Chen

Abstract: In contemporary data-driven environments, the generation and processing of multivariate time series data is an omnipresent challenge, often complicated by time delays between different time series. These delays, originating from a multitude of sources like varying data transmission dynamics, sensor interferences, and environmental changes, introduce significant complexities. Traditional Time Delay Estimation methods, which typically assume a fixed constant time delay, may not fully capture these variabilities, compromising the precision of predictive models in diverse settings.To address this issue, we introduce the Time Series Model Bootstrap (TSMB), a versatile framework designed to handle potentially varying or even nondeterministic time delays in time series modeling. Contrary to traditional approaches that hinge on the assumption of a single, consistent time delay, TSMB adopts a non-parametric stance, acknowledging and incorporating time delay uncertainties. TSMB significantly bolsters the performance of models that are trained and make predictions using this framework, making it highly suitable for a wide range of dynamic and interconnected data environments.Our comprehensive evaluations, conducted on real-world datasets with different types of time delays, confirm the adaptability and effectiveness of TSMB in multiple contexts. These include, but are not limited to, power and occupancy forecasting in intelligent infrastructures, air quality monitoring, and intricate processes like mineral processing. Further diagnostic analyses strengthen the case for the TSMB estimator's robustness, underlining its significance in scenarios where ambiguity in time delays can have a significant impact on the predictive task.