# Bibliography

Armstrong, J. S. (1978). Long-range forecasting: From crystal ball to computer. John Wiley & Sons. [Amazon]

Armstrong, J. S. (Ed.). (2001). Principles of forecasting: A handbook for researchers and practitioners. Kluwer Academic Publishers. [Amazon]

Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31. https://robjhyndman.com/publications/aus-domestic-tourism/

Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis, 120, 70–83. https://robjhyndman.com/publications/cv-time-series/

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed). Hoboken, New Jersey: John Wiley & Sons. [Amazon]

Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd ed). New York, USA: Springer. [Amazon]

Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill.

Buehler, R., Messervey, D., & Griffin, D. (2005). Collaborative planning and prediction: Does group discussion affect optimistic biases in time estimation? Organizational Behavior and Human Decision Processes, 97(1), 47–63. https://doi.org/10.1016/j.obhdp.2005.02.004

Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. J. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–33. http://bit.ly/stl1990

Cleveland, W. S. (1993). Visualizing data. Hobart Press. [Amazon]

Dagum, E. B., & Bianconcini, S. (2016). Seasonal adjustment methods and real time trend-cycle estimation. Springer. [Amazon]

Eroglu, C., & Croxton, K. L. (2010). Biases in judgmental adjustments of statistical forecasts: The role of individual differences. International Journal of Forecasting, 26(1), 116–133. https://doi.org/10.1016/j.ijforecast.2009.02.005

Fildes, R., & Goodwin, P. (2007a). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576. https://doi.org/10.1287/inte.1070.0309

Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. Foresight: The International Journal of Applied Forecasting, (8), 5–10. https://fpc.forecasters.org/2007/10/01/31963/

Franses, P. H., & Legerstee, R. (2013). Do statistical forecasting models for SKU-level data benefit from including past expert knowledge? International Journal of Forecasting, 29(1), 80–87. https://doi.org/10.1016/j.ijforecast.2012.05.008

Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28. https://doi.org/10.1002/for.3980040103

Gardner, E. S. (2006). Exponential smoothing: The state of the art — Part II. International Journal of Forecasting, 22, 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005

Gardner, E. S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10), 1237–1246. https://doi.org/10.1287/mnsc.31.10.1237

Goodwin, P., & Wright, G. (2009). Decision analysis for management judgment (4th ed). Chichester: John Wiley & Sons. [Amazon]

Green, K. C., & Armstrong, J. S. (2007). Structured analogies for forecasting. International Journal of Forecasting, 23(3), 365–376. https://doi.org/10.1016/j.ijforecast.2007.05.005

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed). John Wiley & Sons. [Amazon]

Guerrero, V. M. (1993). Time-series analysis supported by power transformations. Journal of Forecasting, 37–48.

Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed). New York, USA: Springer. [Amazon]

Harris, R., & Sollis, R. (2003). Applied time series modelling and forecasting. Chichester, UK: John Wiley & Sons. [Amazon]

Harvey, N. (2001). Improving judgment in forecasting. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 59–80). Boston, MA: Kluwer Academic Publishers. https://doi.org/10.1007/978-0-306-47630-3_4

Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA. https://doi.org/10.1016/j.ijforecast.2003.09.015

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22. https://doi.org/10.18637/jss.v027.i03

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679–688. https://robjhyndman.com/publications/another-look-at-measures-of-forecast-accuracy/

Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Berlin: Springer-Verlag. http://www.exponentialsmoothing.net

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. New York: Springer. [Amazon]

Kahn, K. B. (2006). New product forecasting: An applied approach. M.E. Sharp. [Amazon]

Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31. https://doi.org/10.1287/mnsc.39.1.17

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y

Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518. https://doi.org/10.1016/j.ijforecast.2006.03.007

Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347–364. https://doi.org/10.1016/j.ijforecast.2007.05.015

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. [Amazon]

Önkal, D., Sayım, K. Z., & Gönül, M. S. (2012). Scenarios as channels of forecast advice. Technological Forecasting and Social Change, 80, 772–788. https://doi.org/10.1016/j.techfore.2012.08.015

Pankratz, A. E. (1991). Forecasting with dynamic regression models. New York, USA: John Wiley & Sons. [Amazon]

Pegels, C. C. (1969). Exponential forecasting: Some new variations. Management Science, 15(5), 311–315. https://doi.org/10.1287/mnsc.15.5.311

Peña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2001). A course in time series analysis. New York, USA: John Wiley & Sons. [Amazon]

Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention-behaviour relationship: Meta-analysis. British Journal of Social Psychology, 33, 405–418. https://doi.org/10.1111/j.2044-8309.1994.tb01037.x

Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, (8), 11–16. https://fpc.forecasters.org/2007/10/01/31964/

Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15, 353–375. https://doi.org/10.1016/S0169-2070(99)00018-7

Sanders, N., Goodwin, P., Önkal, D., Gönül, M. S., Harvey, N., Lee, A., & Kjolso, L. (2005). When and how should statistical forecasts be judgmentally adjusted? Foresight: The International Journal of Applied Forecasting, 1(1), 5–23. https://fpc.forecasters.org/2005/06/01/32051/

Sheather, S. J. (2009). A modern approach to regression with R. New York, USA: Springer. [Amazon]

Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19, 715–725. https://doi.org/10.1016/S0169-2070(03)00003-7

Theodosiou, M. (2011). Forecasting monthly and quarterly time series using STL decomposition. International Journal of Forecasting, 27(4), 1178–1195. https://doi.org/10.1016/j.ijforecast.2010.11.002

Unwin, A. (2015). Graphical data analysis with R. Chapman; Hall/CRC. [Amazon]

Wang, X., Smith, K. A., & Hyndman, R. J. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335–364. https://robjhyndman.com/publications/ts-clustering/

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342. https://doi.org/10.1287/mnsc.6.3.324

1. That is, a percentage is valid on a ratio scale, but not on an interval scale. Only ratio scale variables have meaningful zeros.↩︎

2. Lawrence, Goodwin, O’Connor, & Önkal (2006)↩︎

3. Fildes & Goodwin (2007b)↩︎

4. These are Australian dollar amounts published by the Australian government for 2012.↩︎

5. For further reading, refer to: Rowe (2007); Rowe & Wright (1999)↩︎

6. Buehler, Messervey, & Griffin (2005)↩︎

7. This example is extracted from Kahneman & Lovallo (1993)↩︎

8. Groves et al. (2009)↩︎

9. Randall & Wolff (1994)↩︎

10. GA was an observer on this technical committee for a few years.↩︎

11. Athanasopoulos & Hyndman (2008)↩︎

12. In some books it is called “single exponential smoothing”.↩︎

13. More precisely, if $$\{y_t\}$$ is a stationary time series, then for all $$s$$, the distribution of $$(y_t,\dots,y_{t+s})$$ does not depend on $$t$$.↩︎

14. arc cos is the inverse cosine function. You should be able to find it on your calculator. It may be labelled acos or cos$$^{-1}$$.↩︎

15. A convenient way to produce a time plot, ACF plot and PACF plot in one command is to use the gg_tsdisplay() function.↩︎

16. As already noted, comparing information criteria is only valid for ARIMA models of the same orders of differencing.↩︎

17. Forecasting with cointegrated models is discussed by Harris & Sollis (2003).↩︎

### Bibliography

Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31. https://robjhyndman.com/publications/aus-domestic-tourism/

Buehler, R., Messervey, D., & Griffin, D. (2005). Collaborative planning and prediction: Does group discussion affect optimistic biases in time estimation? Organizational Behavior and Human Decision Processes, 97(1), 47–63. https://doi.org/10.1016/j.obhdp.2005.02.004

Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. Foresight: The International Journal of Applied Forecasting, (8), 5–10. https://fpc.forecasters.org/2007/10/01/31963/

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed). John Wiley & Sons. [Amazon]

Harris, R., & Sollis, R. (2003). Applied time series modelling and forecasting. Chichester, UK: John Wiley & Sons. [Amazon]

Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31. https://doi.org/10.1287/mnsc.39.1.17

Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518. https://doi.org/10.1016/j.ijforecast.2006.03.007

Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention-behaviour relationship: Meta-analysis. British Journal of Social Psychology, 33, 405–418. https://doi.org/10.1111/j.2044-8309.1994.tb01037.x

Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, (8), 11–16. https://fpc.forecasters.org/2007/10/01/31964/

Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15, 353–375. https://doi.org/10.1016/S0169-2070(99)00018-7