Interpolation of Multivariate Time Series with Nearly-Symmetric Time Translations

• Bernardo Teixeira USP
• Marcel Barros USP
• Edson Gomi USP
• Eduardo Tannuri USP
• Fábio Levy Siqueira USP
• Anna Helena Reali Costa USP

Resumo

Modeling complex systems from irregular multivariate time series (MTS), common in engineering and scientific domains, presents significant challenges due to asynchronous measurements, varying sampling rates, and missing data. Building upon architectures that explicitly handle temporal irregularities using time-informed recurrent units, this work introduces a refined approach that fundamentally decomposes the latent state update process. We propose separating the function responsible for incorporating new measurements from the function governing the temporal evolution of the hidden state. Specifically, the temporal translation over a given time delta is modeled using a dedicated transformation parameterized by a matrix constrained to be near-orthogonal. A separate ingestion function updates this time-evolved state with new measurement information when available. This explicit decomposition allows for a more principled representation of continuous-time dynamics, acting as a general regression framework applicable to both imputation and forecasting tasks. We demonstrate through experiments that our model is competitive with state-of-the-art techniques, while providing the desirable properties of near-orthogonal time translations.