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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 |
| 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 |
| Reference manual: | LTAR.pdf |
| Package source: | LTAR_0.1.0.tar.gz |
| Windows binaries: | r-devel: LTAR_0.1.0.zip, r-release: LTAR_0.1.0.zip, r-oldrel: LTAR_0.1.0.zip |
| macOS binaries: | r-release (arm64): LTAR_0.1.0.tgz, r-oldrel (arm64): LTAR_0.1.0.tgz, r-release (x86_64): LTAR_0.1.0.tgz, r-oldrel (x86_64): LTAR_0.1.0.tgz |
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