<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Time Series Forecasting Using Nearest Neighbors</dc:title>
  <dc:title>R package tsfknn version 0.6.0</dc:title>
  <dc:subject>CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries)</dc:subject>
  <dc:description>Allows forecasting time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2019) &lt;doi:10.1007/s10462-017-9593-z&gt;. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. The nearest neighbors used in a prediction
    can be consulted and plotted.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.6.0)</dc:relation>
  <dc:relation>Imports: ggplot2 (&gt;= 3.1.1), graphics, Rcpp, stats, utils</dc:relation>
  <dc:relation>LinkingTo: Rcpp</dc:relation>
  <dc:relation>Suggests: knitr, rmarkdown, testthat (&gt;= 3.0.0)</dc:relation>
  <dc:creator>Francisco Martinez &lt;fmartin@ujaen.es&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Francisco Martinez [aut, cre]</dc:contributor>
  <dc:rights>GPL-2</dc:rights>
  <dc:date>2023-12-20</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=tsfknn</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.tsfknn</dc:identifier>
</oai_dc:dc>
