Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates

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Jiali Mei, Yohann De Castro, Yannig Goude, Georges Hébrail ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2382-2390, 2017.

Abstract

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.

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