mlsanityData to 7 Oct 2026

Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimates

arXiv 2303.08587 ↗First posted 14 Mar 2023cs.LG math.ST stat.MLcited by 2

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Abstract (arXiv)

To model time series accurately is important within a wide range of fields. As the world is generally too complex to be modelled exactly, it is often meaningful to assess the probability of a dynamical system to be in a specific state. This paper presents the Delay-SDE-net, a neural network model based on stochastic delay differential equations (SDDEs). The use of SDDEs with multiple delays as modelling framework makes it a suitable model for time series with memory effects, as it includes memory through previous states of the system. The stochastic part of the Delay-SDE-net provides a basis for estimating uncertainty in modelling, and is split into two neural networks to account for aleatoric and epistemic uncertainty. The uncertainty is provided instantly, making the model suitable for applications where time is sparse. We derive the theoretical error of the Delay-SDE-net and analyze the convergence rate numerically. At comparisons with similar models, the Delay-SDE-net has consistently the best performance, both in predicting time series values and uncertainties.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 98 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

36 of 98 references.

References · 36

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equationscited by 2
  2. 2026Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systemscited by 0