STL Decomposition and TDNN Hybrid Time Series Forecasting
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Overview
About
Maintained by Girish Kumar JhaCRAN 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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13OK
Slowest check: 2.1 min · r-oldrel-windows-x86_64
Status changes
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
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Documentation
In release 0.1.0
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
- Help pages
- 2
- Examples that run
- 50%1 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
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CRAN downloads in the past year
Rank #23,122 · ~6/day · ~168/mo
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21830 days
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2K1 year
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People (4)
Author, Maintainer since 0.1.0 (2021)
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Releases (1)
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.1.0Latest2021-02-24 · current release
- RR 4.0.0 released · 2020-04-24
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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
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citation("stlTDNN")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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