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Found 4 papers, 1 papers with code
Intrinsic Anomaly Detection for Multi-Variate Time Series
We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.
Multi-Objective Model Selection for Time Series Forecasting
By learning a mapping from forecasting models to performance metrics, we show that our method PARETOSELECT is able to accurately select models from the Pareto front -- alleviating the need to train or evaluate many forecasting models for model selection.
Context-invariant, multi-variate time series representations
Modern time series corpora, in particular those coming from sensor-based data, exhibit characteristics that have so far not been adequately addressed in the literature on representation learning for time series.
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
Uncertainty awareness is crucial to develop reliable machine learning models.
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