The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=LTAR to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.