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Found 5 papers, 1 papers with code
Open challenges for Machine Learning based Early Decision-Making research
However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time.
Autoencoder-based time series clustering with energy applications
Time series clustering is a challenging task due to the specific nature of the data.
Nonnegative matrix factorization with side information for time series recovery and prediction
Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features).
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates
Motivated by electricity consumption reconstitution, we propose a new matrix recovery method using nonnegative matrix factorization (NMF).
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries.
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