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Found 7 papers, 5 papers with code
New directions in the applications of rough path theory
This article provides a concise overview of some of the recent advances in the application of rough path theory to machine learning.
Neural Controlled Differential Equations for Online Prediction Tasks
This is fine when the whole time series is observed in advance, but means that Neural CDEs are not suitable for use in \textit{online prediction tasks}, where predictions need to be made in real-time: a major use case for recurrent networks.
Neural CDEs for Long Time Series via the Log-ODE Method
Neural Controlled Differential Equations (Neural CDEs) are the continuous-time analogue of an RNN, just as Neural ODEs are analogous to ResNets.
Neural Rough Differential Equations for Long Time Series
Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions of potentially irregular time series.
Ranked #4 on
Time Series Classification
on EigenWorms
A Generalised Signature Method for Multivariate Time Series Feature Extraction
There is a great deal of flexibility as to how this method can be applied.
Generalised Interpretable Shapelets for Irregular Time Series
The shapelet transform is a form of feature extraction for time series, in which a time series is described by its similarity to each of a collection of `shapelets'.
Neural Controlled Differential Equations for Irregular Time Series
The resulting \emph{neural controlled differential equation} model is directly applicable to the general setting of partially-observed irregularly-sampled multivariate time series, and (unlike previous work on this problem) it may utilise memory-efficient adjoint-based backpropagation even across observations.
