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Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates
Jiali Mei · Yohann De Castro · Yannig Goude · Georges Hébrail

Mon Aug 07 01:30 AM -- 05:00 AM (PDT) @ Gallery #73

Motivated by electricity consumption reconstitution, we propose a new matrix recovery method using nonnegative matrix factorization (NMF). The task tackled here is to reconstitute electricity consumption time series at a fine temporal scale from measures that are temporal aggregates of individual consumption. Contrary to existing NMF algorithms, the proposed method uses temporal aggregates as input data, instead of matrix entries. Furthermore, the proposed method is extended to take into account individual autocorrelation to provide better estimation, using a recent convex relaxation of quadratically constrained quadratic programs. Extensive experiments on synthetic and real-world electricity consumption datasets illustrate the effectiveness of the proposed method.

Author Information

Jiali Mei (EDF R&D & Université Paris-Sud)
Yohann De Castro (LMO)
Yannig Goude (EDF Lab Paris-Saclay)
Georges Hébrail (EDF Lab Paris-Saclay)

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