TSSVM: Time Series Forecasting using SVM Model

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>.

Version:	0.1.0
Depends:	R (≥ 2.3.1), e1071, forecast
Published:	2022-12-02
DOI:	10.32614/CRAN.package.TSSVM
Author:	Mrinmoy Ray [aut, cre], Samir Barman [aut, ctb], Kanchan Sinha [aut, ctb], K. N. Singh [aut, ctb]
Maintainer:	Mrinmoy Ray <mrinmoy4848 at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	TSSVM results

Documentation:

Reference manual:

TSSVM.html , TSSVM.pdf

Downloads:

Package source:	TSSVM_0.1.0.tar.gz
Windows binaries:	r-devel: TSSVM_0.1.0.zip, r-release: TSSVM_0.1.0.zip, r-oldrel: TSSVM_0.1.0.zip
macOS binaries:	r-release (arm64): TSSVM_0.1.0.tgz, r-oldrel (arm64): TSSVM_0.1.0.tgz, r-release (x86_64): TSSVM_0.1.0.tgz, r-oldrel (x86_64): TSSVM_0.1.0.tgz

Reverse dependencies:

Reverse depends:

THETASVM

Linking:

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