EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II

By Everette S. Gardner Jr.. 2006

1,003 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Bayesian forecasting with the Holt–Winters model (2010, methodology): “Other methods, both empirical and analytical, to compute prediction intervals for point forecast from exponential smoothing have been proposed but, in general, the prediction intervals obtained turn to be too narrow (Gardner, 2006).”
Different Approaches to Forecast Interval Time Series: A Comparison in Finance (2011, methodology): “In spite of its straightforward approach, exponential smoothing methods obtain excellent results and usually are the method of choice in automatic forecasts ( Gardner 2006 ).”
Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping ☆ (2015, background): “Despite its theoretical shortcomings, Croston’smethod has been successful in empirical research (see the review in Gardner, 2006) and is widely used in practice.”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, methodology): “and the forecast of yt h + , based on all of the data up to time t, by yt h t ^ + | the component form for the method (A,A) is [19,20]”
Neural network ensemble operators for time series forecasting (2014, methodology): “Exponential smoothing was selected as a benchmark based on its widely demonstrated forecasting accuracy and robustness (Makridakis and Hibon, 2000; Gardner, 2006) and will be named ETS in this work.”
Robust forecasting with exponential and holt-winters smoothing (2009, background): “More details can be found in the review article Gardner (2006).”
Determination of Exponential Smoothing Constant to Minimize Mean Square Error and Mean Absolute Deviation (2011, background): “Gardner (2006) reviewed the research in exponential smoothing since the original work by Brown and Holt and brought the state of the art up to date and invented a complete statistical rationale for exponential smoothing based on a new class of statespace models with a single source of error.”
A general method for addressing forecasting uncertainty in inventory models (2019, background): “It has also been observed more recently that exponential smoothing performs well in forecasting competitions (Ali & Boylan, 2011, 2012; Gardner, 2006), which may be one of the reasons why practitioners typically resort to classical methods.”
Combination of long term and short term forecasts, with application to tourism demand forecasting (2011, methodology): “We chose the exponential smoothing model because it has been quite successful in forecasting business-type time series (Gardner, 2006), and was ranked near the top in the M3 forecasting competition (Makridakis & Hibon, 2000; for example, one model based on Holt's exponential smoothing is among the…”
Temporal Link Prediction Using Matrix and Tensor Factorizations (2010, methodology): “’s linear trend model with additive seasonality, which corresponds to an exponential smoothing method with additive trend and additive seasonality. For a review of exponential smoothing methods, see [Gardner 2006]. An example of forecasting using additive Holt-Winters is shown in Figure 5. The input”
An evaluation of methods for very short-term load forecasting using minute-by-minute British data (2008, methodology): “We implemented the following three non-seasonal exponential smoothing methods: simple, Holt’s (additive trend), and damped Holt’s (damped additive trend) (see Gardner, 2006).”