The R Package knnwtsim: Nonparametric Forecasting With a Tailored Similarity Measure

TL;DR

The paper introduces the R package knnwtsim, which implements a tailored similarity measure for KNN forecasting, especially useful for non-constant or piecewise relationships in time series data.

Contribution

It presents a new similarity measure and functions for KNN forecasting tailored to complex time series with exogenous predictors.

Findings

Effective in forecasting non-constant relationships

Applicable to time series with seasonal and periodic patterns

Includes real and simulated datasets for testing

Abstract

The R package knnwtsim provides functions to implement k nearest neighbors (KNN) forecasting using a similarity metric tailored to the forecasting problem of predicting future observations of a response series where recent observations, seasonal or periodic patterns, and the values of one or more exogenous predictors all have predictive value in forecasting new response points. This paper will introduce the similarity measure of interest, and the functions in knnwtsim used to calculate, tune, and ultimately utilize it in KNN forecasting. This package may be of particular value in forecasting problems where the functional relationships between response and predictors are non-constant or piece-wise and thus can violate the assumptions of popular alternatives. In addition both real world and simulated time series datasets used in the development and testing of this approach have been made available with the package.