Development of a Method to Improve Statistical Forecasts Using Interpolation and Cluster Analysis
Advances in Business and Management Forecasting
ISBN: 978-1-83982-091-5, eISBN: 978-1-83982-090-8
Publication date: 1 September 2021
Abstract
In a previous chapter (Klimberg, Ratick, & Smith, 2018), we introduced a novel approach in which cluster centroids were used as input data for the predictor variables of a multiple linear regression (MLR) used to forecast fleet maintenance costs. We applied this approach to a real data set and significantly improved the predictive accuracy of the MLR model. In this chapter, we develop a methodology for adjusting moving average forecasts of the future values of fleet service occurrences by interpolating those forecast values using their relative distances from cluster centroids. We illustrate and evaluate the efficacy of this approach with our previously used data set on fleet maintenance.
Keywords
Acknowledgements
Acknowledgments
We would like to thank Harvey Smith and ARI for providing the data set.
Citation
Klimberg, R. and Ratick, S. (2021), "Development of a Method to Improve Statistical Forecasts Using Interpolation and Cluster Analysis", Lawrence, K.D. and Klimberg, R.K. (Ed.) Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 14), Emerald Publishing Limited, Leeds, pp. 71-85. https://doi.org/10.1108/S1477-407020210000014006
Publisher
:Emerald Publishing Limited
Copyright © 2021 by Emerald Publishing Limited