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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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