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., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting, 25, 146–166. https://robjhyndman.com/publications/hierarchical-tourism/

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/

Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60–74. https://robjhyndman.com/publications/temporal-hierarchies/

Athanasopoulos, G., Poskitt, D. S., & Vahid, F. (2012). Two canonical VARMA forms: Scalar component models vis-à-vis the echelon form. Econometric Reviews, 31(1), 60–83. https://doi.org/10.1080/07474938.2011.607088

Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Quarterly, 20(4), 451–468. https://doi.org/10.1057/jors.1969.103

Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303–312. https://robjhyndman.com/publications/bagging-ets/

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

Christou, V., & Fokianos, K. (2015). On count time series prediction. Journal of Statistical Computation and Simulation, 85(2), 357–373. https://doi.org/10.1080/00949655.2013.823612

Clemen, R. (1989). Combining forecasts: A review and annotated bibliography with discussion. International Journal of Forecasting, 5, 559–608. https://doi.org/10.1016/0169-2070(89)90012-5

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), 3–73. http://www.jos.nu/Articles/abstract.asp?article=613

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

Crone, S. F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. https://doi.org/10.1016/j.ijforecast.2011.04.001

Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289–303. https://doi.org/10.2307/3007885

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

De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. J American Statistical Association, 106(496), 1513–1527. https://robjhyndman.com/publications/complex-seasonality/

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

Fan, S., & Hyndman, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE Transactions on Power Systems, 27(1), 134–141. https://robjhyndman.com/publications/stlf/

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

Gross, C. W., & Sohl, J. E. (1990). Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9, 233–254. https://doi.org/10.1002/for.3980090304

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]

Hamilton, J. D. (1994). Time series analysis. Princeton University Press, Princeton. [Amazon]

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., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. https://robjhyndman.com/publications/hierarchical/

Hyndman, R. J., & Fan, S. (2010). Density forecasting for long-term peak electricity demand. IEEE Transactions on Power Systems, 25(2), 1142–1153. https://robjhyndman.com/publications/peak-electricity-demand/

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/automatic-forecasting/

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

Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439–454. https://robjhyndman.com/publications/hksg/

Hyndman, R. J., Lee, A., & Wang, E. (2016). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. https://robjhyndman.com/publications/hgts/

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

Lahiri, S. N. (2003). Resampling methods for dependent data. New York, USA: Springer Science & Business Media. [Amazon]

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

Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin: Springer-Verlag. [Amazon]

Lütkepohl, H. (2007). General-to-specific or specific-to-general modelling? An opinion on current econometric terminology. Journal of Econometrics, 136, 234–319. https://doi.org/10.1016/j.jeconom.2005.11.014

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., Sayim, 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]

Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. New York, USA: Springer Science & Business Media. [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]

Shenstone, L., & Hyndman, R. J. (2005). Stochastic models underlying croston’s method for intermittent demand forecasting. Journal of Forecasting, 24(6), 389–402. https://robjhyndman.com/publications/croston/

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/

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed). Springer. [Amazon]

Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2018). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. J American Statistical Association, to appear. https://robjhyndman.com/publications/mint/

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