A state space framework for automatic forecasting using exponential smoothing methods

By Rob J Hyndman, Anne B. Koehler, Ralph D. Snyder, S. Grose. 2002

1,067 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability, Signal Processing, Time-Series & Forecasting

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Citations in the corpus

Optimal combination forecasts for hierarchical time series (2011, methodology): “Applying the statistical framework for exponential smoothing methods developed by Hyndman et al. (2002), we fit a state space model to the training data and obtain independent base forecasts for each of the 15 series for 1–10 steps ahead.”
The Tourism Forecasting Competition (2011, methodology): “• Previous studies have found the forecast intervals obtained to be too narrow; hence, the actual values fall outside the empirical forecast intervals more often than they should (see for example Makridakis and Winkler, 1989; Chatfield, 2001; Hyndman et al., 2002).”
Automatic Time Series Forecasting: The forecast Package for R (2008, methodology): “This was later extended by Gardner (1985), modiﬁed by Hyndman et al. (2002), and extended again by Taylor (2003), giving a total of ﬁfteen methods seen in the following table.”
Applications of functional data analysis: A systematic review (2013, methodology): “In the forecasting process, the authors estimated the coefficients from a time series, with one value representing each time point, and a state space model was used to model and forecast these time series coefficients [23-25,28,130].”
A simple combination of univariate models (2020, methodology): “…four models were considered (Table 1 summarises the R packages and functions used for each model and frequency): • Exponential smoothing (ETS, Hyndman et al., 2002), which selects the best model, underlying one of the fifteen exponential smoothing methods based on the minimisation of a…”
Correlated daily time series and forecasting in the M4 competition (2020, methodology): “In addition we considered Holt-Winters (Hyndman et al., 2002), BATS (Livera et al.”
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “algorithm outperformed the ETS method (Hyndman et al. 2002) for short forecast horizons.”
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016, methodology): “For more detailed descriptions, we refer to Hyndman et al. (2002), Hyndman et al. (2008), and Hyndman and Athanasopoulos (2013).”
Robust forecasting of mortality and fertility rates: A functional data approach (2007, methodology): “Then, conditioning on the observed data I = {yt(xi); t = 1, . . . , n; i = 1, . . . , p} and the set of basis functions Φ, we obtain h-step ahead forecasts of yn+h(x): (4) ŷn,h(x) = E[yn+h(x) | I,Φ] = µ̂(x) + K ∑ k=1 β̃n,k,h φ̂k(x) , where β̃n,k,h denotes the h-step ahead forecast of βn+h,k using…”
Exponential smoothing with a damped multiplicative trend (2003, methodology): “Hyndman et al. (2002) have recently extended this taxonomy to include damped additive trend with either no seasonality, additive seasonality or multiplicative seasonality.”
Unmasking the Theta Method (2003, methodology): “Now, the point forecasts for {Xt} are given by (see, e.g., Hyndman et al., 2001) X̂n(h) = `n + hb.”