TSLSTMplus
1.0.6Long-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
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 29%
- Documented parameters
- 96%
- Return-value docs
- 100%
- References docs
- 29%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 9.0 median / 35 max
Test coverage
Line coverage
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Expression
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Tests / Examples
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Functions
7 5 exported
Complexity
12.9 avg / 35 max
Call network
7 nodes / 6 edges
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
7 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2023-11-21
- Total releases
- 7 / 3 yrs
- License
- GPL-3 OSI
- Download size
- 10 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet