| Type: | Package |
| Title: | Time Series Forecasting using SVM Model |
| Version: | 0.1.0 |
| Depends: | R (≥ 2.3.1), e1071,forecast |
| Description: | Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>. |
| Encoding: | UTF-8 |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Packaged: | 2022-11-29 14:19:23 UTC; pc |
| Author: | Mrinmoy Ray [aut, cre], Samir Barman [aut, ctb], Kanchan Sinha [aut, ctb], K. N. Singh [aut, ctb] |
| Maintainer: | Mrinmoy Ray <mrinmoy4848@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-12-02 08:10:02 UTC |
Auto-Regressive Support Vector Machine
Description
The ARSVM function fit Auto-Regressive Support Vector Machine for univariate time series data.
Usage
ARSVM(data,h)
Arguments
data |
Input univariate time series (ts) data. |
h |
The forecast horizon. |
Details
This package allows you to fit the Auto-Regressive Support Vector Machine for univariate time series.
Value
Optimum lag |
Optimum lag of the considered data |
Model Summary |
Summary of the fitted SVM |
Weights |
weights of the fitted SVM |
Constant |
Constant of the fitted SVM |
MAPE |
Mean Absolute Percentage Error (MAPE) of the SVM |
RMSE |
Root Mean Square Error (RMSE) of fitted SVM |
fitted |
Fitted values of SVM |
forecasted.values |
h step ahead forecasted values employing SVM |
Author(s)
Mrinmoy Ray,Samir Barman, Kanchan Sinha, K. N. Singh
References
Kim, K.(2003). Financial time series forecasting using support vector machines, 55(1-2), 307-319.
See Also
SVM
Examples
data=lynx
ARSVM(data,5)