Package: knnp 2.0.0

Daniel Bastarrica Lacalle

knnp: Time Series Prediction using K-Nearest Neighbors Algorithm (Parallel)

Two main functionalities are provided. One of them is predicting values with k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm. These are carried out in parallel using multiple threads.

Authors:Daniel Bastarrica Lacalle [aut, cre], Javier Berdecio Trigueros [aut], Javier Arroyo Gallardo [aut], Albert Meco Alias [aut]

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manual.pdf |manual.html✨
card.svg |card.png
knnp/json (API)
NEWS

# Install 'knnp' in R:

install.packages('knnp', repos = c('https://grasia.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/grasia/knnp/issues

On CRAN:

knearest-neighbor-algorithm parallel time-series-forecasting

2.70 score 1 stars 8 scripts 178 downloads 4 exports 38 dependencies

Last updated from:cf16fd8a8b. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	143
source / vignettes	OK	207
linux-release-x86_64	ERROR	144
macos-release-arm64	ERROR	140
macos-oldrel-arm64	ERROR	136
windows-devel	ERROR	121
windows-release	ERROR	111
windows-oldrel	ERROR	94
wasm-release	OK	135

Exports:knn knn_forecast knn_param_search knn_past

Dependencies:cli codetools colorspace cpp11 doParallel farver foreach forecast fracdiff generics ggplot2 glue gtable isoband iterators labeling lattice lifecycle lmtest magrittr nlme nnet parallelDist plyr R6 RColorBrewer Rcpp RcppArmadillo RcppParallel rlang S7 scales timeDate urca vctrs viridisLite withr zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generic function to make a prediction for a time series. If a knn model is provided as the first argument, knn_forecast will be directly called. If single values are provided as k and d as no parameter search can be perfomed, knn_forecast will be called automatically. If no values are provided for k and/or d, values 1 to 50 will be used by default.	knn
Creates a matrix to be used for calculating distances. The most recent 'element' is put in the first row of the matrix, the second most recent 'element' in the second row and so on. Therefore, the oldest 'element' is put in the last row.	knn_elements
Predicts next value of the time series using k-nearest neighbors algorithm.	knn_forecast
Searches for the optimal values of k and d for a given time series. First, values corresponding to instants from initial + 1 to the last one are predicted. The first value predicted, which corresponds to instant initial + 1, is calculated using instants from 1 to instant initial; the second value predicted, which corresponds to instant initial + 2, is predicted using instants from 1 to instant initial + 1; and so on until last value, which corresponds to instant n (length of the given time series), is predicted using instants from 1 to instant n - 1. Finally, the error is evaluated between the predicted values and the real values of the series. This version of the optimization function uses a parallelized distances calculation function, and the computation of the predicted values is done parallelizing by the number of d's.	knn_param_search
Predicts values of the time series using k-nearest neighbors algorithm. Values corresponding to instants from initial + 1 to the last one are predicted. The first value predicted, which corresponds to instant initial + 1, is calculated using instants from 1 to instant initial; the second value predicted, which corresponds to instant initial + 2, is predicted using instants from 1 to instant initial + 1; and so on until the last value, which corresponds to instant n (length of the time series), is predicted using instants from 1 to instant n - 1.	knn_past