Papers › Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting

6 May 2024arXiv:2405.03582archive 2025-07-28

Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme

Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challenge to standard deep learning models that operate only on fully observed and regularly sampled time series. In order to capture the continuous dynamics of the irregular time series, many models rely on solving an Ordinary Differential Equation (ODE) in the hidden state. These ODE-based models tend to perform slow and require large memory due to sequential operations and a complex ODE solver. As an alternative to complex ODE-based models, we propose a family of models called Functional Latent Dynamics (FLD). Instead of solving the ODE, we use simple curves which exist at all time points to specify the continuous latent state in the model. The coefficients of these curves are learned only from the observed values in the time series ignoring the missing values. Through extensive experiments, we demonstrate that FLD achieves better performance compared to the best ODE-based model while reducing the runtime and memory overhead. Specifically, FLD requires an order of magnitude less time to infer the forecasts compared to the best performing forecasting model.

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kloetergensc/functional-latent_dynamics officialmentioned in paperpytorch report

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Tasks

Astronomy Irregular Time Series Missing Values Multivariate Time Series Forecasting Time Series Time Series Forecasting

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multivariate Time Series Forecasting	MIMIC-III	FLD	MSE	0.444 ± 0.027	#2 of 5	Archive leaderboard	report
Multivariate Time Series Forecasting	USHCN-Daily	FLD	MSE	0.258	#1 of 8	Archive leaderboard	report

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