Long-Short Term Memory for Time-Series Forecasting, Enhanced
Overview
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) doi:10.1007/s00500-021-06087-4.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
Show 2 earlier changesHide 2 earlier changes
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 29%2 run in a plain check · 5 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 96%
- Return-value docs
- 100%
- Help pages citing references
- 29%
Downloads
Dependencies
Installing TSLSTMplus adds 32 packages (17 with compiled code, 14 MB of source tarballs). Heaviest: keras 3.4 MB, tfruns 1.9 MB, Rcpp 1.8 MB.
Code & Tests
People & History
Package metadata
- First published
- 2023-11-21
- Total releases
- 7 / 3 yrs
- License
- GPL-3 OSI
- Download size
- 10 KB
Cite
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