| Type: | Package |
| Title: | Compute Uniform Error Index |
| Version: | 0.1.0 |
| Author: | Mr. Ankit Kumar Singh [aut], Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut], Ms. Anita Sarkar [aut], Dr. Amrit Kumar Paul [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| Description: | Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | FactoMineR, factoextra, Metrics |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-07-29 11:26:02 UTC; HP |
| Repository: | CRAN |
| Date/Publication: | 2024-07-31 10:00:01 UTC |
Uniform Error Index
Description
Uniform Error Index
Usage
UEI(actual, predicted)
Arguments
actual |
Univariate Data Series |
predicted |
Predicted Data Series from Models |
Value
ErrorSeries: Uniform Error Index Series
ErrorMetrics: Values of Different Error Measures i.e.,Relative Absolute Error (RAE),Mean Absolute Error(MAE),Median Absolute Error (MDAE),Mean Absolute Percent Error (MAPE),Root Mean Squared Error (RMSE),Mean Squared Error (MSE),Symmetric Mean Absolute Percentage Error(SMAPE), Sum of Squared Errors (SSE),Mean Uniform Error Index (MUEI).
References
Yeasin, M. and Paul, R.K., 2024. OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. The Journal of Supercomputing, 80(2), pp.1568-1597.
Examples
library("UEI")
actual<- as.ts(rnorm(50,100,50))
predicted<- as.ts(rnorm(50,110,60))
Result <- UEI(actual, predicted)