Nonnegative Matrix Factorization Method Proposed for Time Series Forecasting with Missing Data
Researchers have introduced the Sliding Mask Method (SMM) combined with nonnegative matrix factorization techniques for forecasting multiple time series containing missing and noisy values. The approach represents time series data as convex combinations of nonnegative vectors called archetypes, with theoretical guarantees on recovery accuracy. The method reportedly outperforms established approaches like Transformers, LSTMs, and SARIMAX on multiple real-world datasets.
A new technical approach to time series forecasting has been proposed that addresses the practical challenge of incomplete and noisy data. The method, called the Sliding Mask Method, reformulates the forecasting problem as a matrix completion task using nonnegative matrix factorization. Two specific estimators—mask Archetypal Matrix Factorization (mAMF) and mask normalized Nonnegative Matrix Factorization (mNMF)—are introduced with mathematical proofs showing that they can recover underlying patterns with error proportional to noise levels. The researchers used a proximal alternating linearized method (PALM) algorithm to compute the solution and compared their approach against contemporary methods including Transformers, LSTM networks, and SARIMAX models across multiple real datasets, claiming superior performance in most experimental scenarios.
What's missing
The study's own limitations and caveats are not detailed in the abstract provided. Specific information about which datasets were used, the magnitude of performance improvements over baseline methods, computational complexity comparisons, and applicability constraints for the nonnegative assumption would provide fuller context for evaluating the practical utility of the approach.
What different sources said
- arXiv cs.LGCenter
Time series forecasting from partial observations via Non-negative Matrix Factorization
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