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tsLSTMx

0.1.0

Predict Time Series Using LSTM Model Including Exogenous Variable to Denote Zero Values

0packages depend
1.8Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Sandip GaraiFirst published 2024-01-121 releasesCRAN page ↗

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>.

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Health

CRAN checks
13OK
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
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README
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Check history
  • OK2026-06-09
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-06-08
    12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

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Dependencies

Declared dependencies
4 external dependencies (excludes base and recommended)
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Code

2,598Lines of code
13Exported functions

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People & History

People (2)
Maintainer (1)
Author, Maintainer
Authors (1)
Release timeline

1 releases. Pick two to compare their code metrics; R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.0Latest
    2026-03-10 · current release

Package metadata

First published
2024-01-12
Total releases
1 / 2 yrs
License
GPL-3 OSI
Download size
not tracked yet
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not tracked yet
With dependencies
not tracked yet