epicasting

Description

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) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.

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Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Tanujit Chakraborty <tanujitisi@gmail.com>’

The Description field contains
  et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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