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It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. It is specifically designed to handle time series with an exogenous variable, allowing users to denote whether data was available for a particular period or not. The package encompasses various functionalities, including hyperparameter tuning, custom loss function support, model evaluation, and one-step-ahead forecasting. With an emphasis on ease of use and flexibility, it empowers users to explore, evaluate, and deploy LSTM models for accurate time series predictions and forecasting in diverse applications. More details can be found in Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
| Version: | 0.1.0 |
| Imports: | tensorflow, AllMetrics, keras, reticulate |
| Published: | 2024-01-12 |
| DOI: | 10.32614/CRAN.package.tsLSTMx |
| Author: | Sandip Garai [aut, cre], Krishna Pada Sarkar [aut] |
| Maintainer: | Sandip Garai <sandipnicksandy at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | tsLSTMx results |
| Reference manual: | tsLSTMx.pdf |
| Package source: | tsLSTMx_0.1.0.tar.gz |
| Windows binaries: | r-devel: tsLSTMx_0.1.0.zip, r-release: tsLSTMx_0.1.0.zip, r-oldrel: tsLSTMx_0.1.0.zip |
| macOS binaries: | r-release (arm64): tsLSTMx_0.1.0.tgz, r-oldrel (arm64): tsLSTMx_0.1.0.tgz, r-release (x86_64): tsLSTMx_0.1.0.tgz, r-oldrel (x86_64): tsLSTMx_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.