TSLSTM
0.1.0Long Short Term Memory (LSTM) Model for Time Series Forecasting
Overview
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. 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
- 0%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 100%
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Code & Tests
- Cyclomatic complexity
- 4.0 median / 4 max
Test coverage
Line coverage
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Expression
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Tests / Examples
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Functions
1 1 exported
Complexity
4 avg / 4 max
Call network
1 nodes / 0 edges
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People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2022-01-13
- Total releases
- 1 / 4 yrs
- License
- GPL-3 OSI
- Download size
- 3.1 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet