Prediction intervals for exponential smoothing using two new classes of state space models 30 January 2003

By Rob J Hyndman, Anne B. Koehler, J. Keith Ord, Ralph D. Snyder. 2003

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Citations in the corpus

The Tourism Forecasting Competition (2011, methodology): “The classification of the exponential smoothing methods in Table 1 originated with Pegels (1969) and was expanded by Gardner (1985), Hyndman et al. (2002) and Taylor (2003). Each of the fifteen methods listed has a trend and a seasonal component.”
Automatic Time Series Forecasting: The forecast Package for R (2008, methodology): “The details of this method, and the associated prediction intervals, are discussed in Hyndman et al. (2005a).”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “Hyndman et al. (2005) used state space models to derive analytical prediction intervals for 15 of the 30 methods, including all the commonly-used methods.”
The admissible parameter space for exponential smoothing models (2008, methodology): “models using a three-letter code, following Hyndman, Koehler, Ord & Snyder (2005) .”
Modelling and Forecasting Australian Domestic Tourism (2006, methodology): “The optimal forecasts from an innovation state space model are identical to those obtained using exponential smoothing methods (Hyndman et al., 2005).”
Bayesian forecasting with the Holt–Winters model (2010, methodology): “This is mainly due to the fact that the different formulas proposed for obtaining prediction intervals (Yar and Chatfield, 1990; Hyndman et al, 2002, 2005) do not take into account the estimation error of the parameters.”
An evaluation of methods for very short-term load forecasting using minute-by-minute British data (2008, methodology): “for producing prediction intervals and density forecasts ( Hyndman et al., 2005 ).”
25 years of time series forecasting (2006, methodology): “Hyndman et al. (2005) used state space models to derive analytical prediction intervals for 15 of the 30 methods, including all the commonly-used methods.”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, background): “Its mean E(y (ω) n+h|n) and variance V(y(ω)n+h|n) are given, after allowing for the Box–Cox transformation, by the equations (Hyndman et al. 2005): E ( y(ω)n+h|n ) = w′Fh−1xn, (12a) V ( y(ω)n+h|n ) = ⎧⎪⎪⎨ ⎪⎪⎩ σ 2, if h = 1, σ 2 [ 1 + h−1∑ j=1 c2j ] , if h ≥ 2, (12b) where cj = w′Fj−1g.”