Journal of Statistics: Advances in Theory and Applications[1] B. Abraham and J. Ledolter, Forecast functions implied by
autoregressive integrated moving average models and other related
forecast procedures, International Statistical Review 54 (1986),
51-66.
[2] B. Abraham and J. Ledolter, On a theory of computation over the
real numbers; NP completeness, recursive functions and universal
machines, A. M. S. Bulletin 21 (1989), 1-46.
[3] P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods,
2nd ed., Springer, New York, 1991.
[4] R. G. Brown, Smoothing, Forecasting and Prediction, Prentice-Hall,
Englewood Cliffs, 1963.
[5] C. Chatfield, Some recent developments in time series analysis,
Journal of Royal Statistical Society 140 (1977), 492-510.
[6] C. Chatfield, Time-Series Forecasting, Chapman & Hall/CRC, Boca
Raton, 2001.
[7] E. S. Gardner, A simple method of computing prediction intervals
for time series forecasts, Management Science 34 (1977), 541-546.
[8] E. S. Gardner, Exponential smoothing: The state of the art (with
discussion), Journal of Forecasting 4 (1985), 1-38.
[9] E. S. Gardner and E. McKenzie, Model identification in exponential
smoothing, Journal of the Operational Research Society 39 (1988),
863-867.
[10] E. S. Gardner, Exponential smoothing: The state of the art - Part
II, International Journal of Forecasting 22 (2006), 637-666.
[11] P. J. Harrison, Exponential smoothing and short-term sales
forecasting, Management Science 13 (1967), 821-842.
[12] C. C. Holt, Forecasting Seasonals and Trends by Exponentially
Weighted Moving Averages, ONR Research Memorandum 52, Carnegie
Institute of Technology, Pittsburgh, Pennsylvania, 1957.
[13] R. J. Hyndman, A. B. Koehler, R. D. Snyder and S. Grose, A state
space framework for automatic forecasting using exponential smoothing
methods, International Journal of Forecasting 18 (2002), 439-454.
[14] P. Jorion, Value at Risk: The New Benchmark for Managing
Financial Risk, 3rd ed., McGraw-Hill, New York, 2006.
[15] R. Koenker, Quantile Regression, Cambridge University Press, New
York, 2005.
[16] S. Kotz, T. J. Kozubowski and K. Podgórski, The Laplace
Distribution and Generalizations: A Revisit with Applications to
Communications, Economics, Engineering, and Finance, Birkhäuser,
Boston, 2001.
[17] S. Makridakis and M. Hibon, Accuracy of forecasting: An empirical
investigation, (with discussion), Journal of the Royal Statistical
Society (A) 142 (1979), 97-145.
[18] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R.
Lewandowski, J. Newton, E. Parzen and R. Winkler, The accuracy of
extrapolation (time series) methods: Results of a forecasting
competition, Journal of Forecasting 1 (1982), 111-153.
[19] S. Makridakis and M. Hibon, Exponential smoothing: The effect of
initial values and loss functions on post-sample forecasting accuracy,
International Journal of Forecasting 7 (1991), 317-330.
[20] E. McKenzie, A comparison of some standard seasonal forecasting
systems, The Statistician 25 (1976), 3-14.
[21] J. F. Muth, Optimal properties of exponentially weighted
forecasts, Journal of American Statistical Association 55 (1960),
299-306.
[22] M. Nerlove and S. Wage, Some observations on adaptive
forecasting, Management Science 10 (1964), 107-224.
[23] J. W. Taylor and D. W. Bunn, A quantile regression approach to
generating prediction intervals, Management Science 45 (1999),
225-237.
[24] J. W. Taylor, Forecasting daily supermarket sales using
exponentially weighted quantile regression, European Journal of
Operational Research 178 (2007), 154-167.
[25] J. W. Taylor, Using exponentially weighted quantile regression to
estimate value at risk and expected shortfall, Journal of Financial
Econometrics 6 (2008), 382-406.
[26] H. Theil and S. Wage, Some observations on adaptive forecasting,
Management Science 10 (1964), 198-206.
[27] A. A. Trindade, Y. Zhu and B. Andrews, Time series models with
asymmetric Laplace innovations, Journal of Statistical Computation and
Simulation 80 (2010), 1317-1333.
[28] P. R. Winters, Forecasting sales by exponentially weighted moving
averages, Management Science 6 (1960), 324-342.
