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Found 12 papers, 0 papers with code
Variational Neural Stochastic Differential Equations with Change Points
We develop two methodologies for modeling and estimating change points in time-series data with distribution shifts.
Fusion of Information in Multiple Particle Filtering in the Presence of Unknown Static Parameters
A simple approach for addressing this problem is to augment the unknown static parameters as auxiliary states that are jointly estimated with the time-varying parameters of interest.
A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shifts.
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
Given a time series dataset, the proposed method jointly learns the unknown change points and the parameters of distinct neural SDE models corresponding to each change point.
Augment on Manifold: Mixup Regularization with UMAP
Evaluations across diverse regression tasks show that UMAP Mixup is competitive with or outperforms other Mixup variants, show promise for its potential as an effective tool for enhancing the generalization performance of deep learning models.
MADS: Modulated Auto-Decoding SIREN for time series imputation
Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled.
Deep Gaussian Mixture Ensembles
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty.
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.
StyleTime: Style Transfer for Synthetic Time Series Generation
In this work, a novel formulation of time series style transfer is proposed for the purpose of synthetic data generation and enhancement.
HyperTime: Implicit Neural Representation for Time Series
We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data.
