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Found 3 papers, 1 papers with code
WindDragon: Enhancing wind power forecasting with Automated Deep Learning
Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids.
Interpretable time series neural representation for classification purposes
In this work, we propose a set of requirements for a neural representation of univariate time series to be interpretable.
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors.
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