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

Documentation:

Reference manual:

LTAR.pdf

Downloads:

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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