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05/25/2023
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Ensembling is among the most popular tools in machine learning (ML) due ...
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02/21/2023
Learning Physical Models that Can Respect Conservation Laws
Recent work in scientific machine learning (SciML) has focused on incorp...
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02/04/2023
Cross-Frequency Time Series Meta-Forecasting
Meta-forecasting is a newly emerging field which combines meta-learning ...
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12/15/2022
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Transformer-based models have gained large popularity and demonstrated p...
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12/14/2022
Guiding continuous operator learning through Physics-based boundary constraints
Boundary conditions (BCs) are important groups of physics-enforced const...
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12/14/2021
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow
The transport of traffic flow can be modeled by the advection equation. ...
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02/13/2021
Attention-based Domain Adaptation for Time Series Forecasting
Recent years have witnessed deep neural networks gaining increasing popu...
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06/12/2019
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS)[<https://gluon-ts.mxnet.io>], a...
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05/28/2019
Deep Factors for Forecasting
Producing probabilistic forecasts for large collections of similar and/o...
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11/30/2018