knn: Generic function to make a prediction for a time series. If a...

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

Description

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.

Usage

knn(
  y,
  k = 1:50,
  d = 1:50,
  initial = NULL,
  distance = "euclidean",
  error_measure = "MAE",
  weight = "proportional",
  v = 1,
  threads = 1
)

Arguments

y	A time series or a trained kNN model generated by the knn_param_search function. In case that a model is provided the knn_forecast function will be automatically called.
k	Values of k's to be analyzed or chosen k for knn forecasting. Default value is 1 to 50.
d	Values of d's to be analyzed or chosen d for knn forecasting. Default value is 1 to 50.
initial	Variable that determines the limit of the known past for the first instant predicted.
distance	Type of metric to evaluate the distance between points. Many metrics are supported: euclidean, manhattan, dynamic time warping, camberra and others. For more information about the supported metrics check the values that 'method' argument of function parDist (from parallelDist package) can take as this is the function used to calculate the distances. Link to package info: https://cran.r-project.org/web/packages/parallelDist Some of the values that this argument can take are "euclidean", "manhattan", "dtw", "camberra", "chord".
error_measure	Type of metric to evaluate the prediction error. Five metrics supported: ME Mean Error RMSE Root Mean Squared Error MAE Mean Absolute Error MPE Mean Percentage Error MAPE Mean Absolute Percentage Error
weight	Type of weight to be used at the time of calculating the predicted value with a weighted mean. Three supported: proportional, average, linear. proportional the weight assigned to each neighbor is inversely proportional to its distance average all neighbors are assigned with the same weight linear nearest neighbor is assigned with weight k, second closest neighbor with weight k-1, and so on until the least nearest neighbor which is assigned with a weight of 1.
v	Variable to be predicted if given multivariate time series.
threads	Number of threads to be used when parallelizing, default is 1

Value

A matrix of errors, optimal k and d. All tested ks and ks and all the used metrics.

Examples

knn(AirPassengers, 1:5, 1:3)
knn(LakeHuron, 1:10, 1:6)

knnp documentation built on Jan. 11, 2020, 9:26 a.m.