Forecasting models and prediction intervals for the multiplicative Holt-Winters method

By Anne B. Koehler, Ralph D. Snyder, K. Ord. 2001

130 citations recorded by the source corpus.

Subfields: Atmospheric Science, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/aeaeec15de162f0568d44ec3e38fc1f339db218e

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In Atmospheric Science

In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “Koehler et al. (2001) also derived an approximate formula for the forecast variance for the multiplicative Holt-Winters’ method, differing from Chatfield & Yar (1991) only in how the standard deviation of the one-step-ahead forecast error is estimated.”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “As Koehler et al. (2001) and Hyndman et al. (2002) observed, their state-space models are not unique and many other such models could be formulated.”
Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data (2007, background): “A lot of different formulae have been proposed for obtaining prediction intervals, but simulated and empirical studies (Yar & Chatfield, 1990 ; Ord et al , 1997 ; Koehler et al , 2001 ) have shown that the proposed intervals tend to be too narrow, in the sense that more observations than expect”
25 years of time series forecasting (2006, methodology): “Koehler et al. (2001) also derived an approximate formula for the forecast variance for the multiplicative Holt-Winters’ method, differing from Chatfield & Yar (1991) only in how the standard deviation of the one-step-ahead forecast error is estimated.”
Forecasting and operational research: a review (2008, background): “The formulation permits the explicit calculation of prediction intervals (Koehler et al, 2001), thereby removing a long-standing criticism of socalled ad hoc smoothing methods.”
A state space framework for automatic forecasting using exponential smoothing methods (2002, unknown): “One of the interesting results from our the following derive an equivalent state space formulation space equations for both the additive error and with a single source of error for each of the 12 multiplicative error versions of each method.”
Prediction intervals for exponential smoothing using two new classes of state space models 30 January 2003 (2003, unknown): “Hyndman, Koehler, Ord and Snyder: 30 January 2003 2”