10 citations · 35 across the 8 of their papers we have counts for
4 papers · 1 filter
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Bryan Lim, Sercan O. Arik, Nicolas Loeff +1
Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series…
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs
Bryan Lim, Stefan Zohren, Stephen Roberts
Despite recent innovations in network architectures and loss functions, training RNNs to learn long-term dependencies remains difficult due to challenges with gradient-based optimi…
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
Bryan Lim, Stefan Zohren, Stephen Roberts
While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this…
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
Bryan Lim, Stefan Zohren, Stephen Roberts
Despite the recent popularity of deep generative state space models, few comparisons have been made between network architectures and the inference steps of the Bayesian filtering…