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Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) <doi:10.48550/arXiv.2206.10696> <doi:10.1016/j.chaos.2023.113124>.
| Version: | 0.1.0 |
| Depends: | datasets |
| Imports: | forecast, Metrics, stats, wavelets |
| Suggests: | ggplot2 |
| Published: | 2023-03-22 |
| DOI: | 10.32614/CRAN.package.epicasting |
| Author: | Madhurima Panja [aut], Tanujit Chakraborty [aut, cre, cph] |
| Maintainer: | Tanujit Chakraborty <tanujitisi at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| In views: | Epidemiology |
| CRAN checks: | epicasting results |
| Reference manual: | epicasting.pdf |
| Package source: | epicasting_0.1.0.tar.gz |
| Windows binaries: | r-devel: epicasting_0.1.0.zip, r-release: epicasting_0.1.0.zip, r-oldrel: epicasting_0.1.0.zip |
| macOS binaries: | r-release (arm64): epicasting_0.1.0.tgz, r-oldrel (arm64): epicasting_0.1.0.tgz, r-release (x86_64): epicasting_0.1.0.tgz, r-oldrel (x86_64): epicasting_0.1.0.tgz |
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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.