Exponential smoothing with a damped multiplicative trend

By James W. Taylor. 2003

316 citations recorded by the source corpus.

Subfields: Applied Mathematics, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Meta-learning for time series forecasting and forecast combination (2010, methodology): “• Taylor’s exponential smoothing is a more recently introduced exponential smoothing algorithm with a trend dampened by a factor φ , using a multiplicative approach and a growth rate R [42].”
Forecasting: theory and practice (2020, methodology): “Some studies have extended the classical ARIMA and Exponential Smoothing (ETS) methods to accommodate MSC. Multiple/multiplicative Seasonal ARIMA (MSARIMA) model is an extension of ARIMA for the case of MSC (Taylor, 2003b).”
Improving forecasting by estimating time series structural components across multiple frequencies (2014, methodology): “ETS is a family of methods, designed for forecasting time series with different characteristics, covering a wide range of additive and multiplicative combinations of level, trend, seasonality and noise (Gardner, 1985; Taylor, 2003; Gardner, 2006).”
Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals (2011, unknown): “Building on earlier works (see, for example, Ord, Koehler, & Snyder, 1997; Hyndman, Koehler, Snyder, & Grose, 2002; and Taylor, 2003), Hyndman et al. (2008) present a comprehensive statistical framework for building state space models for exponential smoothing methods, in which prediction intervals…”
Determination of Exponential Smoothing Constant to Minimize Mean Square Error and Mean Absolute Deviation (2011, methodology): “Taylor (2003) investigated a new damped multiplicative trend approach.”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “Taylor (2003) extended the linear version of the Holt–Winters method to incorporate a second seasonal component as follows: yt = t−1 + bt−1 + s(1)t + s(2)t + dt, (1a) t = t−1 + bt−1 + αdt, (1b) bt = bt−1 + βdt, (1c) s(1)t = s(1)t−m1 + γ1dt, (1d) s(2)t = s(2)t−m2 + γ2dt, (1e) where m1 and m2 are the…”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “Taylor (2003a) also obtained somewhat disconcerting results with the Gardner–McKenzie procedure.”
Forecasting the novel coronavirus COVID-19 (2020, methodology): “We produce forecasts using models from the exponential smoothing family [5, 6].”
Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy (2014, background): “However, both Tashman and Kruk (1996) and Taylor (2003) argue that there can be value in allowing 1   as it could be suited to time series with strong increasing trends.”
Forecasting and operational research: a review (2008, methodology): “Taylor’s (2007) exponentially smoothed approach is shown to”
25 years of time series forecasting (2006, methodology): “The damped multiplicative methods of Taylor (2003) provide the only genuinely new exponential smoothing methods over this period.”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “The shape of the combined forecast error distribution and the corresponding stochastic behaviour was studied by de Menezes & Bunn (1998) and Taylor & Bunn (1999). For non-normal forecast error distributions skewness emerges as a relevant criterion for specifying the method of combination.”