3 citations · 5 across the 3 of their papers we have counts for
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cs.LG2022
Deep Recurrent Modelling of Granger Causality with Latent Confounding
Zexuan Yin, Paolo Barucca
Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer pote…
cs.LG2022★ 3 cited
Neural Generalised AutoRegressive Conditional Heteroskedasticity
Zexuan Yin, Paolo Barucca
We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in…
cs.LG2021
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting
Zexuan Yin, Paolo Barucca
Time series forecasting based on deep architectures has been gaining popularity in recent years due to their ability to model complex non-linear temporal dynamics. The recurrent ne…