theta_arnn: Hybrid Theta ARNN Forecasting Model

In hybridts: Hybrid Time Series Forecasting Using Error Remodeling Approach

Hybrid Theta ARNN Forecasting Model

Description

Hybrid Theta ARNN Forecasting Model

Usage

theta_arnn(y, n, PI = FALSE, ret_fit = FALSE)

Arguments

y	A numeric vector or time series
n	An integer specifying the forecast horizon
PI	A logical flag (default = FALSE) for generating the prediction interval.
ret_fit	A logical flag specifying that the fitted values of the model on the training set should be returned if true, otherwise, false (default)

Value

The forecast of the time series of size n is generated along with the optional output of fitted values (ret_fit = TRUE) and confidence interval (PI = TRUE) for the forecast.

References

• Bhattacharyya, A., Chakraborty, T., & Rai, S. N. (2022). Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. Nonlinear Dynamics, 1-16.

• Bhattacharyya, A., Chattopadhyay, S., Pattnaik, M., & Chakraborty, T. (2021, July). Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

Examples

theta_arnn(y = datasets::lynx, n = 3)

hybridts documentation built on April 11, 2023, 5:59 p.m.