Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs

TL;DR

This paper introduces Physiome-ODE, a large benchmark dataset for irregularly sampled multivariate time series forecasting based on biological ODEs, addressing limitations of existing small datasets and revealing new insights into ODE-based models.

Contribution

It develops a methodology to generate challenging IMTS datasets from biological ODEs and creates the first large-scale benchmark, enabling better evaluation of ODE-based forecasting models.

Findings

Existing benchmarks are too small and unchallenging.

ODE-based models outperform simple baselines on Physiome-ODE.

Physiome-ODE enables meaningful differentiation between forecasting models.

Abstract

State-of-the-art methods for forecasting irregularly sampled time series with missing values predominantly rely on just four datasets and a few small toy examples for evaluation. While ordinary differential equations (ODE) are the prevalent models in science and engineering, a baseline model that forecasts a constant value outperforms ODE-based models from the last five years on three of these existing datasets. This unintuitive finding hampers further research on ODE-based models, a more plausible model family. In this paper, we develop a methodology to generate irregularly sampled multivariate time series (IMTS) datasets from ordinary differential equations and to select challenging instances via rejection sampling. Using this methodology, we create Physiome-ODE, a large and sophisticated benchmark of IMTS datasets consisting of 50 individual datasets, derived from real-world ordinary differential equations from research in biology. Physiome-ODE is the first benchmark for IMTS forecasting that we are aware of and an order of magnitude larger than the current evaluation setting of four datasets. Using our benchmark Physiome-ODE, we show qualitatively completely different results than those derived from the current four datasets: on Physiome-ODE ODE-based models can play to their strength and our benchmark can differentiate in a meaningful way between different IMTS forecasting models. This way, we expect to give a new impulse to research on ODE-based time series modeling.