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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 |
| 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 |
| Reference manual: | TSSVM.pdf |
| 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 |
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