[1] B.L. Bowerman and R. T. O' Connell (1993). Forecasting and Time Series: An Applied Approach, 3rd edition. Duxbury Press.
[2] G.E.P. Box and G.M. Jenkins (1994). Time Series Analysis: Forecasting and Control, 3rd Edition. Prentice Hall.
[3] J.D. Cryer (1986). Time Series Analysis. Duxbury Press.
[4] N.R. Farnum and L.W. Stanton (1989). Quantitative Forecasting Methods. PWS-Kent.
[5] G.M. Ljung and G.E.P. Box (1978). "On a Measure of Lack of Fit in Time Series Models," Biometrika, 65, 67-72.
[6] S. Makridakis, S.C. Wheelwright, and R. J. Hyndman (1998). Forecasting: Methods and Applications. Wiley.
[7] D.W. Marquardt (1963). "An Algorithm for Least Squares Estimation of Nonlinear Parameters," Journal Soc. Indust. Applied Mathematics, 11, 431-441.
[8] W.Q. Meeker, Jr. (1977). "TSERIES-A User-oriented Computer Program for Identifying, Fitting and Forecasting ARIMA Time Series Models," ASA 1977 Proceedings of the Statistical Computing Section.
[9] W.Q. Meeker, Jr. (1977). TSERIES User's Manual, Statistical Laboratory, Iowa State University.
[10] W. Vandaele (1983). Applied Time Series and Box-Jenkins Models. Academic Press, Inc.
The ARIMA algorithm is based on the fitting routine in the TSERIES package written by Professor William Q. Meeker, Jr., of Iowa State University [8], [9]. We are grateful to Professor Meeker for his help in the adaptation of his routine to Minitab.