Optimization Best Method Selection for Times Series Data Forecasting in Production Management

Rorim Panday
Faculty of Economics, Management, Bhayangkara Jakarta Raya University

Abstract

Forecasting is widely used in business, to predict future sales, use of raw materials,
determine the amount of production, estimate financial needs, and human resource
needs. Basically, forecasting is done to minimize business risks. The time series data
is most widely used in business. There are many forecasting methods, however, not all
forecasting methods are suitable for particular data. In this paper, we present how to
choose a forecasting method that is fit for a particular data. The principle of selecting
a fit model is to compare standard deviations between one method and another.

Abstract

Forecasting is widely used in business, to predict future sales, use of raw materials,
determine the amount of production, estimate financial needs, and human resource
needs. Basically, forecasting is done to minimize business risks. The time series data
is most widely used in business. There are many forecasting methods, however, not all
forecasting methods are suitable for particular data. In this paper, we present how to
choose a forecasting method that is fit for a particular data. The principle of selecting
a fit model is to compare standard deviations between one method and another.

Keyword : Forecasting–data time series–fit model–standard deviation

How to Cite
(1)
Panday, R. Optimization Best Method Selection for Times Series Data Forecasting in Production Management. sslej 2019, 4, 1-8.
Online First
Feb 13, 2019
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References

[1] Granvik AR. Forecasting Exchange Rates. Arcada Polytechnic;
2010.
[2] Panagiotopoulos A. OPTIMISING TIME SERIES FORECASTS
THROUGH Apostolos Panagiotopoulos , MSc Thesis
submitted to the University of Nottingham for the degree
of Doctor of. Philosophy; 2011.
[3] Armstrong J. Principles of Forecasting. Massachusetts:
Kluwer Academic Publishers; 2001.
[4] Makridakis S, Wheelright S, Hyndman R. Forecasting:
Methods and Application. John Wiley & Sons, Inc; 1998. .
[5] Heizer J, Render B. Operations Managementitio). New
Jersey: Pearson Education; 2004.
[6] Mendenhall W, Reinmuth JE, Beaver RJ. Statistics for
Management and Economicsitio). Belmont: Wadsworth Inc;
1993.
[7] Krajewski L, Ritzman L, Malhotra M. Operations Management:
Processes and Value. Pearson Prentice Hall; 2007.
.
[8] Lapide L. Evolution of the Forecasting Function. The Journal
of Business Forecasting. 2006;25(1):22–28.
[9] Fisher M, Hammond J, Obermeyer W, Raman A. Making
Supply Meet Demand in an Uncertain World”. Harvard
Business Review. 1994;73(3):83–93.
[10] Lind DA, Marchal WG, Mason RD. Statistical Techniques
in Business & Economics. New York: McGraw-Hill; 2002.
[11] R JF, Boyland JE, S MME. Some properties of a simple
moving average when applied to forecasting a time series.
The Journal of the Operational Research Society.
1999;50(12):1267.
[12] Mathew A. Demand Forecasting For Economic Order Quantity
in Inventory Management. Nternational Journal of Scientific
and Research Publications. 2013;3(10):1–6.
[13] Gardner E. Evaluating Forecasting performance in an Inventory
Control System. Management Science. 1990;36(4):490–
499.
[14] Fildes R, Beard C. Forecasting Systems for Production and
Inventory Control. International Journal of Operations and
Production Management. 1992;12(5):4–27.
[15] Wacker J, Lummus R. Sales Forecasting for Strategic Resources
Planning. International Journal of Operations and
Production Management. 2002;22(9).
[16] Datta S, Granger CWJ, Graham DP, Sagar N, Doody P,
Slone R. Forecasting and. London: Springer International
Publishing; 2009. Available from: https://doi.org/https.
Social

References

[1] Granvik AR. Forecasting Exchange Rates. Arcada Polytechnic;
2010.
[2] Panagiotopoulos A. OPTIMISING TIME SERIES FORECASTS
THROUGH Apostolos Panagiotopoulos , MSc Thesis
submitted to the University of Nottingham for the degree
of Doctor of. Philosophy; 2011.
[3] Armstrong J. Principles of Forecasting. Massachusetts:
Kluwer Academic Publishers; 2001.
[4] Makridakis S, Wheelright S, Hyndman R. Forecasting:
Methods and Application. John Wiley & Sons, Inc; 1998. .
[5] Heizer J, Render B. Operations Managementitio). New
Jersey: Pearson Education; 2004.
[6] Mendenhall W, Reinmuth JE, Beaver RJ. Statistics for
Management and Economicsitio). Belmont: Wadsworth Inc;
1993.
[7] Krajewski L, Ritzman L, Malhotra M. Operations Management:
Processes and Value. Pearson Prentice Hall; 2007.
.
[8] Lapide L. Evolution of the Forecasting Function. The Journal
of Business Forecasting. 2006;25(1):22–28.
[9] Fisher M, Hammond J, Obermeyer W, Raman A. Making
Supply Meet Demand in an Uncertain World”. Harvard
Business Review. 1994;73(3):83–93.
[10] Lind DA, Marchal WG, Mason RD. Statistical Techniques
in Business & Economics. New York: McGraw-Hill; 2002.
[11] R JF, Boyland JE, S MME. Some properties of a simple
moving average when applied to forecasting a time series.
The Journal of the Operational Research Society.
1999;50(12):1267.
[12] Mathew A. Demand Forecasting For Economic Order Quantity
in Inventory Management. Nternational Journal of Scientific
and Research Publications. 2013;3(10):1–6.
[13] Gardner E. Evaluating Forecasting performance in an Inventory
Control System. Management Science. 1990;36(4):490–
499.
[14] Fildes R, Beard C. Forecasting Systems for Production and
Inventory Control. International Journal of Operations and
Production Management. 1992;12(5):4–27.
[15] Wacker J, Lummus R. Sales Forecasting for Strategic Resources
Planning. International Journal of Operations and
Production Management. 2002;22(9).
[16] Datta S, Granger CWJ, Graham DP, Sagar N, Doody P,
Slone R. Forecasting and. London: Springer International
Publishing; 2009. Available from: https://doi.org/https.
Social
How to Cite
(1)
Panday, R. Optimization Best Method Selection for Times Series Data Forecasting in Production Management. sslej 2019, 4, 1-8.