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Found 10 papers, 6 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.
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data
In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data.
Unsupervised Model Selection for Time-series Anomaly Detection
The practical problem of selecting the most accurate model for a given dataset without labels has received little attention in the literature.
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets.
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
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.
DASGrad: Double Adaptive Stochastic Gradient
Adaptive moment methods have been remarkably successful for optimization under the presence of high dimensional or sparse gradients, in parallel to this, adaptive sampling probabilities for SGD have allowed optimizers to improve convergence rates by prioritizing examples to learn efficiently.
Double Adaptive Stochastic Gradient Optimization
We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.
