On the performance of temporal demand aggregation when optimal forecasting is used

Bahman Rostami-Tabar; M.Z. Babai; Mohammad M. Ali; J. E. Boylan

On the performance of temporal demand aggregation when optimal forecasting is used

Bahman Rostami-Tabar, M.Z. Babai, Mohammad M. Ali, J. E. Boylan

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding

Abstract

Earlier research has shown that non-overlapping temporal aggregation of auto-correlated demand can improve the forecast accuracy of single exponential smoothing, especially for negative or low positive autocorrelation parameter. In this paper, we analyse the impact of non-overlapping temporal aggregation when an optimal forecasting method is used. We consider an AR(1) demand process and a minimum mean square error (MMSE) forecasting method. The expressions of the mean square error (MSE) before and after the aggregation of the demand are derived. The numerical results of the comparison of the MSEs show that by using the optimal MMSE forecasting method, regardless of the aggregation level and the autocorrelation parameter, the non-overlapping temporal aggregation approach is outperformed by the non-aggregation one.

Original language	English
Title of host publication	ILS 2016 - 6th International Conference on Information Systems, Logistics and Supply Chain
Publication status	Published - 2016
Event	International Conference on Information Systems, Logistics and Supply Chain - Bordeaux, France Duration: 1 Jun 2016 → 4 Jun 2016

Conference

Conference	International Conference on Information Systems, Logistics and Supply Chain
Abbreviated title	ILS 2016
Country/Territory	France
City	Bordeaux
Period	1/06/16 → 4/06/16

Bibliographical note

The full text is available from: ils2016conference.com/wp-content/uploads/2015/03/ILS2016_SB05_4.pdf

Keywords

Demand aggregation
Forecast accuracy
Mean square error

Access to Document

Cite this

Rostami-Tabar, B, Babai, MZ, Ali, MM & Boylan, JE 2016, On the performance of temporal demand aggregation when optimal forecasting is used. in ILS 2016 - 6th International Conference on Information Systems, Logistics and Supply Chain. International Conference on Information Systems, Logistics and Supply Chain, Bordeaux, France, 1/06/16. <http://ils2016conference.com/after-ils-2016/>

@inproceedings{fe781e7c327a4537b06bc52cd85e1860,

title = "On the performance of temporal demand aggregation when optimal forecasting is used",

abstract = "Earlier research has shown that non-overlapping temporal aggregation of auto-correlated demand can improve the forecast accuracy of single exponential smoothing, especially for negative or low positive autocorrelation parameter. In this paper, we analyse the impact of non-overlapping temporal aggregation when an optimal forecasting method is used. We consider an AR(1) demand process and a minimum mean square error (MMSE) forecasting method. The expressions of the mean square error (MSE) before and after the aggregation of the demand are derived. The numerical results of the comparison of the MSEs show that by using the optimal MMSE forecasting method, regardless of the aggregation level and the autocorrelation parameter, the non-overlapping temporal aggregation approach is outperformed by the non-aggregation one.",

keywords = "Demand aggregation, Forecast accuracy, Mean square error",

author = "Bahman Rostami-Tabar and M.Z. Babai and Ali, {Mohammad M.} and Boylan, {J. E.}",

note = "The full text is available from: ils2016conference.com/wp-content/uploads/2015/03/ILS2016_SB05_4.pdf; International Conference on Information Systems, Logistics and Supply Chain, ILS 2016 ; Conference date: 01-06-2016 Through 04-06-2016",

year = "2016",

language = "English",

booktitle = "ILS 2016 - 6th International Conference on Information Systems, Logistics and Supply Chain",

}

TY - GEN

T1 - On the performance of temporal demand aggregation when optimal forecasting is used

AU - Rostami-Tabar, Bahman

AU - Babai, M.Z.

AU - Ali, Mohammad M.

AU - Boylan, J. E.

N1 - The full text is available from: ils2016conference.com/wp-content/uploads/2015/03/ILS2016_SB05_4.pdf

PY - 2016

Y1 - 2016

N2 - Earlier research has shown that non-overlapping temporal aggregation of auto-correlated demand can improve the forecast accuracy of single exponential smoothing, especially for negative or low positive autocorrelation parameter. In this paper, we analyse the impact of non-overlapping temporal aggregation when an optimal forecasting method is used. We consider an AR(1) demand process and a minimum mean square error (MMSE) forecasting method. The expressions of the mean square error (MSE) before and after the aggregation of the demand are derived. The numerical results of the comparison of the MSEs show that by using the optimal MMSE forecasting method, regardless of the aggregation level and the autocorrelation parameter, the non-overlapping temporal aggregation approach is outperformed by the non-aggregation one.

AB - Earlier research has shown that non-overlapping temporal aggregation of auto-correlated demand can improve the forecast accuracy of single exponential smoothing, especially for negative or low positive autocorrelation parameter. In this paper, we analyse the impact of non-overlapping temporal aggregation when an optimal forecasting method is used. We consider an AR(1) demand process and a minimum mean square error (MMSE) forecasting method. The expressions of the mean square error (MSE) before and after the aggregation of the demand are derived. The numerical results of the comparison of the MSEs show that by using the optimal MMSE forecasting method, regardless of the aggregation level and the autocorrelation parameter, the non-overlapping temporal aggregation approach is outperformed by the non-aggregation one.

KW - Demand aggregation

KW - Forecast accuracy

KW - Mean square error

M3 - Conference proceeding

BT - ILS 2016 - 6th International Conference on Information Systems, Logistics and Supply Chain

T2 - International Conference on Information Systems, Logistics and Supply Chain

Y2 - 1 June 2016 through 4 June 2016

ER -

On the performance of temporal demand aggregation when optimal forecasting is used

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this