A Novel Approach to Forecasting Regression and Cluster Analysis

Authors

Ronald K. Klimberg
Samuel Ratick
Harvey Smith

Document Type

Article

Abstract

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series dataset contains numerous observations of important predictor variables that can effectively be classified into groups based on their values. In such situations, cluster analysis is often employed to improve the MLR models predictive accuracy, usually by creating separate regressions for each cluster. We introduce a novel approach in which we use the clusters and cluster centroids as input data for the predictor variables to improve the predictive accuracy of the MLR model. We illustrate and test this approach with a real dataset on fleet maintenance.

Publication Title

Advances in Business and Management Forecasting

Publication Date

1-1-2017

Volume

12

First Page

87

Last Page

101

ISSN

1477-4070

DOI

10.1108/S1477-407020170000012006

Keywords

cluster analysis, forecasting, regression

Repository Citation

Klimberg, Ronald K.; Ratick, Samuel; and Smith, Harvey, "A Novel Approach to Forecasting Regression and Cluster Analysis" (2017). Sustainability and Social Justice. 459.
https://commons.clarku.edu/faculty_idce/459