# Irregular Time Series

15 papers with code • 0 benchmarks • 0 datasets

Irregular Time Series

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# 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.

# 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.

# On Neural Differential Equations

Topics include: neural ordinary differential equations (e. g. for hybrid neural/mechanistic modelling of physical systems); neural controlled differential equations (e. g. for learning functions of irregular time series); and neural stochastic differential equations (e. g. to produce generative models capable of representing complex stochastic dynamics, or sampling from complex high-dimensional distributions).

# Path Imputation Strategies for Signature Models of Irregular Time Series

The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.

# 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 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.

# Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows

Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation.

# Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting

Neural networks inspired by differential equations have proliferated for the past several years.

# Modeling Irregular Time Series with Continuous Recurrent Units

Recurrent neural networks (RNNs) are a popular choice for modeling sequential data.

# SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification

With the increasingly available large-scale cancer genomics datasets, machine learning approaches have played an important role in revealing novel insights into cancer development.