Forecasting with Exponential Smoothing: The State Space Approach

By Robin John Hyndman, Anne B. Koehler, J. Ord, Ralph D. Snyder. 2008

383 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8025360749af135629de9d740523a14d26ab7f08

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Citations in the corpus

A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting (2020, unknown): “Keeping in mind that the M4 series all have positive values, the models of Holt (Gardner, 2006) and Holt and Winters (Hyndman, Koehler, Ord, & Snyder, 2008) with multiplicative seasonality were chosen.”
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion (2024, unknown): “Method Exchange Solar Electricity Traffic Taxi Wikipedia VES [35] 0.005 ± 0.000 0.900 ± 0.003 0.880 ± 0.004 0.350 ± 0.002 - - VAR [44] 0.005 ± 0.000 0.830 ± 0.006 0.039 ± 0.001 0.290 ± 0.001 - - VAR-Lasso [44] 0.012 ± 0.000 0.510 ± 0.006 0.025 ± 0.000 0.150 ± 0.002 - 3.100 ± 0.004 GARCH [61] 0.023…”
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting (2021, methodology): “…VAR-Lasso a Lasso regularized VAR , • GARCH (van der Weide, 2002) a multivariate conditional heteroskedastic model and • VES a innovation state space model (Hyndman et al., 2008); as well as deep learning based methods namely: • KVAE (Fraccaro et al., 2017) a variational autoencoder to represent…”
On the identification of sales forecasting models in the presence of promotions (2015, unknown): “Since the 1950s the use of Exponential Smoothing for forecasting has been the most popular forecasting method used in business and industry (Gardner, 2006; Hyndman et al, 2008).”
Forecasting and operational research: a review (2008, background): “What has in fact been achieved is a unified statistical framework in which all the variants of exponential smoothing are embedded (Hyndman et al, 2008).”
Bayesian Intermittent Demand Forecasting for Large Inventories (2016, unknown): “Note that ISSMs are characterized by a single Gaussian innovation variable ε t per time step.”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, unknown): “[5] (amongst others) developed a statistical framework for all exponential smoothing methods.”
Forecasting with temporal hierarchies (2017, unknown): “The DGP we generate from is an ETS(A,Ad,A) model: µt = `t−1 + φbt−1 + st−m `t = `t−1 + φbt−1 + αεt bt = φbt−1 + βεt st = st−m + γεt ŷt+h|t = `t + φhbt + st−m+h+m where φh = φ+ · · ·+ φh and h+m = [ (h− 1) mod m ] + 1 (for more details see Hyndman et al., 2008).”
Predictability of monthly temperature and precipitation using automatic time series forecasting methods (2018, methodology): “(Hyndman et al. 2008) or AutoRegressive Integrated Moving Average (ARIMA) (and”
Improving time series forecasting using LSTM and attention models (2021, unknown): “Comparing the results of the LSTM model indicates that it achieves a better AR than the standard forecasting methods such as ETS, ARIMA, and MLP.”