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Spyros Makridakis, S. Wheelwright, Victor McGee (1979)
Forecasting: Methods and Applications
G. Box, G. Jenkins, Gregory Reinsel, Greta Ljung (1978)
Time Series Analysis: Forecasting and ControlThe Statistician, 27
R. Fildes, R. Winkler, R. Carbone, Spyros Makridakis, M. Hibon, E. Parzen, A. Andersen, R. Lewandowski, J. Newton (1982)
The Accuracy of Extrapolation (Time Series) Methods
C. Wasson (1974)
Dynamic competitive strategy & product life cycles
Essam Mahmoud (1984)
Accuracy in forecasting: A surveyJournal of Forecasting, 3
J. Chambers, S. Mullick, Donald Smith (1984)
An executive's guide to forecasting
Spyros Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler (1982)
The accuracy of extrapolation (time series) methods: Results of a forecasting competitionJournal of Forecasting, 1
Shirley Kallek (1978)
An Overview of the Objectives and Framework of Seasonal Adjustment
L. Coopersmith (1983)
Forecasting time series which are inherently discontinuousJournal of Forecasting, 2
F. Fisher (1967)
The identification problem in econometrics
Russell Krueger (1980)
“Seasonal Adjustment of Irregular Time Series: U.S. Merchandise Trade.” Unpublished manuscript—presented at the third International Time Series Meeting
P. Winters (1960)
Forecasting Sales by Exponentially Weighted Moving AveragesManagement Science, 6
John W. Tukey (1977)
Exploratory Data Analysis
J. Armstrong, J. Wiley, Sons, N. York, Chichester Brisbane, Toronto Singapore, Jon Armstrong, Scott, Xerox Kodak, He (1981)
Long-Range Forecasting: From Crystal Ball to Computer
G. Day (1981)
The Product Life Cycle: Analysis and Applications IssuesJournal of Marketing, 45
E. Dagum (1978)
Modelling, Forecasting and Seasonally Adjusting Economic Time Series with the X-11 ARIMA MethodThe Statistician, 27
J. Mckeown, Kenneth Lorek (1978)
A COMPARATIVE ANALYSIS OF THE PREDICTIVE ABILITY OF ADAPTIVE FORECASTING, RE‐ESTIMATION, AND RE‐IDENTIFICATION USING BOX‐JENKINS TIME‐SERIES ANALYSIS ON QUARTERLY EARNINGS DATADecision Sciences, 9
James P. Cleary (1981)
The Beginning Forecaster
T. Cook, R. Russell (1978)
A SIMULATION AND STATISTICAL ANALYSIS OF STOCHASTIC VEHICLE ROUTING WITH TIMING CONSTRAINTSDecision Sciences, 9
P. Huber (1964)
Robust Estimation of a Location ParameterAnnals of Mathematical Statistics, 35
I. J. Terpenning (1979)
SABL—A Resistant Seasonal Analysis of Economic Time Series
Abstract The development of a sales forecasting system involves three major steps. The first step is to obtain prior sales data and to identify the model that will best forecast the patterns that exist in the data. The second step is to estimate parameter values for the selected model by analyzing the prior sales data. The third step is to test the accuracy of the model by use of the prior sales data. Each of the steps requires use of prior data. In all three steps, there is a basic assumption that the past data represent some underlying process that can be identified and modeled. In some cases the past data may not represent the underlying process, and the forecasting process is seriously distorted. Some frequent causes of distorted data are 1) accounting methods that are used to record or collect the data, 2) marketing tactics such as promotions which that create outliers, 3) limits on production capacity that cause stockouts. This paper looks at events and actions that may distort data used for sales forecasting and at the resulting impact the events and actions may have on forecasting accuracy.
Journal of the Academy of Marketing Science – Springer Journals
Published: Sep 1, 1988
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