Nonnegative matrix factorization with side information for time series recovery and prediction - Université Toulouse 1 Capitole Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Knowledge and Data Engineering Année : 2018

Nonnegative matrix factorization with side information for time series recovery and prediction

Résumé

Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features). We consider general linear measurement settings, and propose a framework which models non-linear relationships between features and the response variables. We extend previous theoretical results to obtain a sufficient condition on the identifiability of the NMF in this setting. Based the classical Hierarchical Alternating Least Squares~(HALS) algorithm, we propose a new algorithm (HALSX, or Hierarchical Alternating Least Squares with eXogeneous variables) which estimates the factorization model. The algorithm is validated on both simulated and real electricity consumption datasets as well as a recommendation dataset, to show its performance in matrix recovery and prediction for new rows and columns.

Dates et versions

hal-01686429 , version 1 (17-01-2018)

Identifiants

Citer

Jean-Marc Azaïs, Yohann de Castro, Yannig Goude, Georges Hébrail, Jiali Mei. Nonnegative matrix factorization with side information for time series recovery and prediction. IEEE Transactions on Knowledge and Data Engineering, 2018. ⟨hal-01686429⟩
439 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More