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Found 7 papers, 4 papers with code
Forecasting Response to Treatment with Global Deep Learning and Patient-Specific Pharmacokinetic Priors
Our global-local architecture improves over patient-specific models by 9. 2-14. 6%.
Hierarchically Coherent Multivariate Mixture Networks
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings.
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings.
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed.
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
We validate our proposed method, DMIDAS, on high-frequency healthcare and electricity price data with long forecasting horizons (~1000 timestamps) where we improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors.
