Deep Learning Model for Time Series Forecasting
Overview
Provides deep learning models for time series forecasting using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). These models capture temporal dependencies and address vanishing gradient issues in sequential data. The package enables efficient forecasting for univariate time series. For methodological details see Jaiswal and co-authors (2022). doi:10.1007/s00521-021-06621-3.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespaces in Imports field not imported from: ‘reticulate’ ‘tensorflow’ All declared Imports should be used.
- NOTE11 OK, 2 NOTE2026-06-09
- ERROR10 OK, 2 NOTE, 1 ERROR2026-06-08
- NOTE6 OK, 2 NOTE2026-04-14
Documentation
- Help pages
- 4
- Examples that run
- 0%4 never run
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Installing TSdeeplearning adds 65 packages (36 with compiled code, 42 MB of source tarballs). Heaviest: ggplot2 6.0 MB, keras 3.4 MB, smooth 3.4 MB. 1 more not on CRAN is not measured.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- unarchivedReturned to CRAN2026-04-13
- 1.0.1Latest
- archivedRemoved from CRAN2025-11-23issues were not corrected in time
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.02022-09-09
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-09-09
- Total releases
- 2 / 4 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 1.3 KB / 1 file
- Download size
- 7.1 KB
Cite
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citation("TSdeeplearning")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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