Supervised Low-Rank Semi-nonnegative Matrix Factorization with Frequency Regularization for Forecasting Spatio-temporal Data

We propose a novel methodology for forecasting spatio-temporal data using supervised semi-nonnegative matrix factorization (SSNMF) with frequency regularization. Matrix factorization is employed to decompose spatio-temporal data into spatial and temporal components. To improve clarity in the tempora...

Full description

Saved in:
Bibliographic Details
Published in:Journal of scientific computing 2024-08, Vol.100 (2), p.29, Article 29
Main Authors: Kim, Keunsu, Lyu, Hanbaek, Kim, Jinsu, Jung, Jae-Hun
Format: Article
Language:English
Subjects:
Algorithms
Computational Mathematics and Numerical Analysis
Constraints
Data analysis
Factorization
Forecasting
Fourier transforms
Frequency domain analysis
Geophysics
GRACE (experiment)
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Methodology
Partial differential equations
Regularization
Regularization methods
Spatiotemporal data
Theoretical
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a novel methodology for forecasting spatio-temporal data using supervised semi-nonnegative matrix factorization (SSNMF) with frequency regularization. Matrix factorization is employed to decompose spatio-temporal data into spatial and temporal components. To improve clarity in the temporal patterns, we introduce a nonnegativity constraint on the time domain along with regularization in the frequency domain. Specifically, regularization in the frequency domain involves selecting features in the frequency space, making an interpretation in the frequency domain more convenient. We propose two methods in the frequency domain: soft and hard regularizations, and provide convergence guarantees to first-order stationary points of the corresponding constrained optimization problem. While our primary motivation stems from geophysical data analysis based on GRACE (Gravity Recovery and Climate Experiment) data, our methodology has the potential for wider application. Consequently, when applying our methodology to GRACE data, we find that the results with the proposed methodology are comparable to previous research in the field of geophysical sciences but offer clearer interpretability.
ISSN:0885-7474
1573-7691
DOI:10.1007/s10915-024-02565-7