Wavelet Based K-Nearest Neighbor Model
Overview
The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) DOI:10.1007/s00521-017-3289-9.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
Show 2 earlier changesHide 2 earlier changes
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-10
Documentation
- Help pages
- 1
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Installing WaveletKNN adds 80 packages (48 with compiled code, 58 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
People & History
- 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
- 0.1.0Latest2023-04-05 · current release
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-04-05
- Total releases
- 1 / 3 yrs
- License
- GPL-3 OSI
- Download size
- 2.9 KB
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