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Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates

Abstract : Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual series. Furthermore, our algorithm is extended to take into account individual autocorrelation to provide better estimation, using a recent convex relaxation of quadratically constrained quadratic program. Extensive experiments on synthetic and real-world electricity consumption datasets illustrate the effectiveness of our matrix recovery algorithms.
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Contributor : Yohann de Castro Connect in order to contact the contributor
Submitted on : Wednesday, January 17, 2018 - 1:38:38 PM
Last modification on : Friday, January 21, 2022 - 12:56:02 PM

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  • HAL Id : hal-01686437, version 1
  • ARXIV : 1610.01492


Yohann de Castro, Yannig Goude, Georges Hébrail, Jiali Mei. Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates. ICML 2017 - 34th International Conference on Machine Learning, Aug 2017, Sydney, Australia. pp.1-9. ⟨hal-01686437⟩



Les métriques sont temporairement indisponibles