LTAR: Tensor Forecasting Functions
A set of tools for forecasting the next step in a multidimensional setting using tensors. In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long). Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE <doi:10.1109/ICMLA52953.2021.00078>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.2.0) |
| Imports: |
vars, stats, rTensor, rTensor2, gsignal |
| Published: |
2023-08-21 |
| DOI: |
10.32614/CRAN.package.LTAR |
| Author: |
Kyle Caudle [aut, cre],
Randy Hoover [ctb],
Jackson Cates [ctb] |
| Maintainer: |
Kyle Caudle <kyle.caudle at sdsmt.edu> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
LTAR results [issues need fixing before 2025-06-11] |
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