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stlTDNN

0.1.0

STL Decomposition and TDNN Hybrid Time Series Forecasting

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1.9Kdownloads / year
test coverage
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Overview

About
Maintained by Girish Kumar JhaFirst published 2021-02-241 releasesCRAN page ↗

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). doi:10.1007/s00521-012-1264-z, Xiong T, Li C, Bao Y (2018). doi:10.1016/j.neucom.2017.11.053.

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Health

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1NOTE12OK
Failing flavors
  • NOTE r-devel-linux-x86_64-debian-gcc
Slowest check: 2.4 min · r-devel-linux-x86_64-fedora-clang
Code health
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Tests · ratio 0.00
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Coverage
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Documentation · exports
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Dependencies · direct
Check history
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • 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

Documentation

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Examples that run
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Documented parameters
100%
Return-value docs
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References docs
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Dependencies

Declared dependencies
2 external dependencies (excludes base and recommended)
Depends (1)
R >= 2.10
Imports (2)
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none
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none
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Code & Tests

People & History

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Authors (2)
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Contributors (3)
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Package Timeline

1 release. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.1.0Latest
    2026-03-10 · current release
  • R
    R 4.5.0 released · 2025-04-11

Package metadata

First published
2021-02-24
Total releases
1 / 5 yrs
License
GPL-3 OSI
Minimum R
≥ 2.10
Bundled data
1.3 KB / 1 file
Download size
4.4 KB
Installed size
not tracked yet
With dependencies
not tracked yet
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