Forecasting: Methods and Applications

By Spyros Makridakis, Steven Wheelwright, V. Mcgee. 1979

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Citations in the corpus

Automatic Time Series Forecasting: The forecast Package for R (2008, methodology): “Equation (1c) is slightly diﬀerent from the usual Holt-Winters equations such as those in Makridakis et al. (1998) or Bowerman, O’Connell, and Koehler (2005).”
Prediction of elastic displacement response spectra in Europe and the Middle East (2007, methodology): “Therefore, it was decided to apply negative exponential smoothing [22] for each regression coefficient against period.”
Theta intelligent forecasting information system (2003, unknown): “If the time-series presents statistically significant seasonal behavior then it is deseasonalised via the classical multiplicative decomposition method (Makridakis, 1998).”
Application of a Robust Algorithm for Dynamic State Estimation of a Power System (2002, methodology): “where is an -dimensional nonzero diagonal matrix; is an -dimensional column vector; is a white Gaussian sequence with zero mean and covariance matrix . The parameter matrix and can be identified on-line by using a linear exponential smoothing technique of forecasting [ 16 ].”
Forecasting electricity consumption in New Zealand using economic and demographic variables (2005, methodology): “[15] state that multicollinearity is first and foremost a computational problem.”
LONG-RANGE FORECASTING From Crystal Ball to Computer (2017, background): “(LRF 278) CERF, Christopher and NAVASKY, Victor, [1984], The Experts Speak. New York: Pantheon Books . . . A collection of inaccurate statements and forecasts by experts. (LRF 87,91,103) CERULLO, Michael J. and AVILA, Alfonso, [1975], "Sales forecasting practices: A survey," Managerial Planning, vol”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “This ETS algorithm overcomes limitations from the previous models of exponential smoothing which failed to provide a method for easily calculating prediction intervals (Makridakis et al., 1998).”