Forecasting Sales by Exponentially Weighted Moving Averages

By P. R. Winters. 1960

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Citations in the corpus

Time-series forecasting with deep learning: a survey (2020, methodology): “While traditional methods have focused on parametric models informed by domain expertise—such as autoregressive (AR) [6], exponential smoothing [7,8] or structural time-series models [9]— modern machine learning methods provide a means to learn temporal dynamics in a purely data-driven manner [10].”
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019, methodology): “The statistical modeling approaches based on exponential smoothing and its different flavors are well established and are often considered a default choice in the industry [10, 11, 31].”
Modelling and Forecasting Australian Domestic Tourism (2006, background): “Exponential smoothing was proposed in the late 1950s (see the pioneering works of Brown, 1959; Holt, 1957; Winters, 1960) and has motivated some of the most successful forecasting methods.”
Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy (2014, methodology): “It is common practice in exponential smoothing models to restrict the smoothing parameters   ,   to the   0,1 interval (Holt, 2004; Winters, 1960).”
Modeling and Forecasting of COVID-19 Growth Curve in India (2020, methodology): “We propose the following models, for modelling of COVID-19 cases [see Holt (1957) and Winters (1960)].”
'Horses for Courses' in demand forecasting (2014, methodology): “The variable with the least influence is trend, in particular for the Holt, Holt-Winters and Linear Trend methods as the values of the corresponding regression coefficients are small.”
Damped trend exponential smoothing: A modelling viewpoint (2010, unknown): “In a series of three papers (Gardner & McKenzie, 1985, 1988, 1989), we developed new versions of the Holt–Winters (Holt, 2004; Winters, 1960) methods of exponential smoothing that damp the trend as the forecast horizon increases.”
Multi-Horizon Time Series Forecasting with Temporal Attention Learning (2019, unknown): “ClassicaltimeseriesforecastingapproachesincludeHolt-Winters method [15, 30] and ARIMA [5].”
EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II (2006, unknown): “Table 1 contains equations for the standard methods of exponential smoothing, all of which are extensions of the work of Brown (1959, 1963), Holt (1957), and Winters (1960).”
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift (2022, methodology): “As an example of the statistical models, exponential smoothing forecasting (Holt, 2004; Winters, 1960) is a well-established benchmark for predicting future values.”
Advances in Forecasting Under Instability (2012, unknown): “The initial value for the recursion can be the initial observation.27 Holt (1957) and Winters (1960) generalized the approach to include a local linear trend.”
Forecasting models and prediction intervals for the multiplicative Holt-Winters method (2001, methodology): “The estimates that we use are found by the procedures originally proposed by Winters (1960) for his initial estimates.”