General exponential smoothing and the equivalent arma process

By Ed McKenzie. 1984

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Citations in the corpus

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019, methodology): “For a comprehensive treatment of traditional methods, we refer the readers to [13, 19, 4, 18, 12] and the references there in.”
Time series forecasting using a hybrid ARIMA and neural network model (2003, methodology): “In addition, various exponential smoothing models can be implemented by ARIMA models [26].”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “, McKenzie, 1984). The damped multiplicative methods of Taylor (2003) provide the only genuinely new exponential smoothing methods over this period.”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “We adopt the Gardner & McKenzie (1985) damped trend with damping 130 parameter φ , but follow the suggestion in Snyder (2006) to supplement it with a long-run 131 trend b. This change ensures that predictions of future values of the short-run trend bt 132 converge to the long-run trend b instead of ”
25 years of time series forecasting (2006, methodology): “…& Sweet, 1989; Makridakis & Hibon, 1991), proposals for new methods of estimation or initialization (Ledolter & Abraham, 1984), evaluation of the forecasts (Sweet & Wilson, 1988; McClain, 1988), or has concerned statistical models that can be considered to underly the methods (e.g. McKenzie, 1984).”
Long-term Forecasting with TiDE: Time-series Dense Encoder (2023, methodology): “1984) and the Box-Jenkins methodology (Box and Jenkins, 1968) belong in this category.”
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks (2017, background): “ARIMAmodels are not only adaptive to various exponential smoothing techniques [25] but also flexible enough to subsume other types of time series models including autoregression (AR), moving average (MA) and Autoregressive Moving Average (ARMA).”
A comparative study of linear and nonlinear models for aggregate retail sales forecasting (2003, methodology): “In addition, it has been shown that the popular Winter’s additive and multiplicative exponential smoothing models can be implemented by the equivalent ARIMA models (McKenzie, 1984; Bowerman and O’Connell, 1993).”
A decoder-only foundation model for time-series forecasting (2023, methodology): “We create generators for ARMA (McKenzie, 1984) processes, seasonal patterns (mixture of sines and cosines of different frequencies), trends (linear, exponential with a few change-points) and step functions.”