  We consider nonnegative time series forecasting framework. Based on recent
advances in Nonnegative Matrix Factorization (NMF) and Archetypal Analysis, we
introduce two procedures referred to as Sliding Mask Method (SMM) and Latent
Clustered Forecast (LCF). SMM is a simple and powerful method based on time
window prediction using Completion of Nonnegative Matrices. This new procedure
combines low nonnegative rank decomposition and matrix completion where the
hidden values are to be forecasted. LCF is two stage: it leverages archetypal
analysis for dimension reduction and clustering of time series, then it uses
any black-box supervised forecast solver on the clustered latent
representation. Theoretical guarantees on uniqueness and robustness of the
solution of NMF Completion-type problems are also provided for the first time.
Finally, numerical experiments on real-world and synthetic data-set confirms
forecasting accuracy for both the methodologies.
