Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates

Yohann de Castro 1, 2 Yannig Goude 3, 1 Georges Hébrail 4, 3 Jiali Mei 1
1 LMO - Laboratoire de Mathématiques d'Orsay
2 MOKAPLAN - Méthodes numériques pour le problème de Monge-Kantorovich et Applications en sciences sociales
CEREMADE - CEntre de REcherches en MAthématiques de la DEcision, Inria de Paris
3 EDF R&D - EDF R&D
4 BILab - Laboratoire de Business Intelligence Télécom-ParisTech/EDF
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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Conference papers
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https://hal.inria.fr/hal-01686437
Contributor : Yohann de Castro <>
Submitted on : Wednesday, January 17, 2018 - 1:38:38 PM
Last modification on : Wednesday, January 23, 2019 - 2:39:26 PM

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

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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⟩

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