| Title: | Fitting and Forecasting of Grey Model for Multivariate Time Series Data |
|---|---|
| Description: | Grey model is commonly used in time series forecasting when statistical assumptions are violated with a limited number of data points. The minimum number of data points required to fit a grey model is four observations. This package fits Grey model of First order and One Variable, i.e., GM (1,1) for multivariate time series data and returns the parameters of the model, model evaluation criteria and h-step ahead forecast values for each of the time series variables. For method details see, Akay, D. and Atak, M. (2007) <DOI:10.1016/j.energy.2006.11.014>, Hsu, L. and Wang, C. (2007).<DOI:10.1016/j.techfore.2006.02.005>. |
| Authors: | Pradip Basak [aut, cph, cre], Nobin Chandra Paul [aut, cph] |
| Maintainer: | Pradip Basak <pradip@ubkv.ac.in> |
| License: | GPL (>= 2.0) |
| Version: | 0.1.0 |
| Built: | 2025-03-02 07:03:54 UTC |
| Source: | CRAN |
The multigreyfit function fits the GM (1,1) model for multivariate time series data and computes its model parameters as well as different model evaluation criteria.
multigreyfit(data)multigreyfit(data)
data |
Input multivariate time series data. |
This function returns the parameters of the fitted grey model, i.e., a and b which are known as control variable and developing coefficient respectively for each of the of the variables in the multivariate timeseries data. This function also returns the mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and relative mean absolute percentage error (MAPE) values of the fitted GM (1, 1) model for each of the variables in the multivariate time series data.
A list with the following components:
a |
Control variable, i.e., parameter of fitted GM (1, 1) model for each of the time series variables. |
b |
Developing coefficient, i.e., parameter of fitted GM (1, 1) model for each of the time series variables. |
mse |
Mean Squared Error (MSE) for each time series variable. |
rmse |
Root Mean Square Error (RMSE) of fitted GM (1, 1) model for each of the time series variables |
mape |
Relative Mean Absolute Percentage Error (MAPE) of fitted GM (1, 1) model for each of the time series variables |
mae |
Mean Absolute Error (MAE) of fitted GM (1, 1) model for each of the time series variables |
Akay, D. and Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.<DOI:10.1016/j.energy.2006.11.014>
Deng, J. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1-24.
Hsu, L.C. and Wang, C.H. (2007). Forecasting the output of integrated circuit industry using a grey model improved by Bayesian analysis. Technological Forecasting and Social Change, 74(6), 843-853.<DOI:10.1016/j.techfore.2006.02.005>
# Example data xt <- c(640, 684, 713, 745, 809, 811, 883, 893, 904, 935, 1044, 1069) yt <- c(50, 64, 93, 113, 131, 152, 164, 201, 224, 268, 286, 290) zt <- c(550,504,493,413,431,352,364,301,294,268,286,230) data <- cbind(xt, yt, zt) # Apply the multigreyfit function multigreyfit(data)# Example data xt <- c(640, 684, 713, 745, 809, 811, 883, 893, 904, 935, 1044, 1069) yt <- c(50, 64, 93, 113, 131, 152, 164, 201, 224, 268, 286, 290) zt <- c(550,504,493,413,431,352,364,301,294,268,286,230) data <- cbind(xt, yt, zt) # Apply the multigreyfit function multigreyfit(data)
The multigreyforecast function computes the h-step ahead forecast values for each of the variables in the multivariate time series data.
multigreyforecast(data, h)multigreyforecast(data, h)
data |
Input multivariate time series data. |
h |
The forecast horizon. |
This function returns the h-step ahead forecasted values of the fitted GM (1, 1) model for each of the variables in the multivariate time series data.
A h-step ahead forecast values
forecast |
h-step ahead forecast values corresponding to each of the variables in the multivariate time series data. |
Akay, D. and Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.<DOI:10.1016/j.energy.2006.11.014>
Deng, J. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1-24.
Hsu, L.C. and Wang, C.H. (2007). Forecasting the output of integrated circuit industry using a grey model improved by Bayesian analysis. Technological Forecasting and Social Change, 74(6), 843-853.<DOI:10.1016/j.techfore.2006.02.005>
# Example data xt <- c(640, 684, 713, 745, 809, 811, 883, 893, 904, 935, 1044, 1069) yt <- c(50, 64, 93, 113, 131, 152, 164, 201, 224, 268, 286, 290) zt <- c(550,504,493,413,431,352,364,301,294,268,286,230) data <- cbind(xt, yt, zt) # Apply the multigreyfit function multigreyforecast(data, h=3)# Example data xt <- c(640, 684, 713, 745, 809, 811, 883, 893, 904, 935, 1044, 1069) yt <- c(50, 64, 93, 113, 131, 152, 164, 201, 224, 268, 286, 290) zt <- c(550,504,493,413,431,352,364,301,294,268,286,230) data <- cbind(xt, yt, zt) # Apply the multigreyfit function multigreyforecast(data, h=3)