Description: A KNN implementaion which allows continuous responses, the specification of the distance used to calculate nearest neighbors (euclidean, binary, etc.), the aggregation method used to summarize repsonse (majority class, mean, etc.) and the method of handling ties (all, random selection, etc.).
Packaged: Mon Feb 23 11:44:08 2009; hornik

Packaged: Mon Feb 26 11:12:11 2007; atbroo

Title: Time Series Prediction using K-Nearest Neighbors Algorithm
person("Daniel", "Bastarrica Lacalle", email="danibast@ucm.es", role=c("aut")),
person("Javier", "Berdecio Trigueros", email="javierberdeciot@gmail.com", role=c("aut", "cre"))
Imports: parallelDist, forecast, stats, utils, doParallel, foreach
Description: 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.
Author: Daniel Bastarrica Lacalle [aut],
Maintainer: Javier Berdecio Trigueros <javierberdeciot@gmail.com>

person("Daniel", "Bastarrica Lacalle", email="danibast@ucm.es", role=c("aut", "cre")),
person("Javier", "Berdecio Trigueros", email="javierberdeciot@gmail.com", role=c("aut")),
person("Javier", "Arroyo Gallardo", email="javier.arroyo@fdi.ucm.es", role=c("aut")),
person("Albert", "Meco Alias", email="ameco@ucm.es", role=c("aut"))
Imports: parallelDist, forecast, stats, utils, doParallel, foreach,
Author: Daniel Bastarrica Lacalle [aut, cre],
Maintainer: Daniel Bastarrica Lacalle <danibast@ucm.es>

Title: K Nearest Neighbor Forecasting with a Tailored Similarity Metric
Description: Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <https://github.com/mtrupiano1/knnwtsim/blob/main/README.md>.