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Found 5 papers, 2 papers with code
Neural Granger Causality
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
A sufficient condition for Granger non-causality in this setting is that all of the outgoing weights of the input data, the past lags of a series, to the first hidden layer are zero.
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary regions.
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed.
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data.
