LTAR (version 0.1.0)

LTARpred: Forecast for a 3D Tensor Autoregression Model

Description

Using a historical 3D tensor, the LTARpred function will forecast h steps into the future.

Usage

LTARpred(p, tnsr, h, type = c("const", "trend", "both", "none"), season = NULL)

Value

A Tensor-class object which contains the h step forecasts.

Arguments

p

: Number of time series lags

tnsr

: A 3D tensor

h

: Number of steps to forecast

type

Type of deterministic regressors to include.

season

: Inclusion of centered seasonal dummy variables (integer value of frequency).

Author

Kyle Caudle

Randy Hoover

Jackson Cates

References

Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021, December). 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.

Examples

require(rTensor)
data(tensor)
tnsr <- as.tensor(tensor)
result <- LTARpred(p=5,tnsr,h=2,type="trend",season=12)