Forecasting Trends in Time Series

By Everette S. Gardner Jr., Ed McKenzie. 1985

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Citations in the corpus

Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors (2018, methodology): “However, given a completed smoothed time series (straight line), the coefficient of SES will be always forced to one.”
Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy (2014, methodology): “Although such exponential smoothing systems with damping parameters had been noted earlier (see for instance, Gardner (1985), Gilchrist (1976), and Roberts (1982)), Gardner and McKenzie (1985) were the first to present both a theoretical and an empirical investigation of the system.”
Modelling and Forecasting Australian Domestic Tourism (2006, methodology): “The damped trend model was proposed by Gardner and McKenzie (1985) as a modification to Holt’s linear model.”
Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data (2007, methodology): “For the period between January 1949 and December 1999 these data are also available from Grubb (Grubb and Mason, 2001). Following a common approach to dealing with this type of seasonality, Grubb and Mason (2001) consider a power transformation (Box and Cox, 1964) to make both the seasonality and th”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “In large samples, several versions of exponential smoothing are equivalent to exponentially weighted or DLS regression models (Brown, 1963; Gardner & McKenzie, 1985, 1988).”
Forecasting and operational research: a review (2008, methodology): “Two articles, Gardner and McKenzie (1985) and Collopy and Armstrong (1992), proposed new extrapolative”
Different Approaches to Forecast Interval Time Series: A Comparison in Finance (2011, methodology): “The exponential smoothing method with damped additive trend (ESDAT), proposed in Gardner and McKenzie (1985) , can also be adapted to ITS.”
Findings from Evidence-Based Forecasting: Methods for Reducing Forecast Error (2006, unknown): “When tested on M-Competition data (Makridakis et al 1982) and with data from four other data sets, the Median Absolute Percentage Error (MdAPE) was reduced by 17% for one-year-ahead forecasts and by over 40% for six-year-ahead forecasts (Armstrong & Collopy 1993).”