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stat.ML2020★ 10 cited
Autoencoder-based time series clustering with energy applications
Guillaume Richard, Benoît Grossin, Guillaume Germaine +2
Time series clustering is a challenging task due to the specific nature of the data. Classical approaches do not perform well and need to be adapted either through a new distance m…
stat.ML2016★ 2 cited
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates
Jiali Mei, Yohann De Castro, Yannig Goude +1
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix en…