Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors

By Evangelos Spiliotis, Vassilios Assimakopoulos, Konstantinos Nikolopoulos. 2018

28 citations recorded by the source corpus.

Abstract In this paper, we discuss how extrapolation can be advanced by using some of the most successful elements and paradigms from the forecasting literature. We propose a new hybrid method that utilises: a) the decomposition approach of the Theta method, but instead of considering a linear trend we allow for nonlinear trends, b) rather than employing the extrapolation method on the raw data, we first apply smoothing to the data, and c) when seasonality is present, we employ the shrinkage approach to the derived indices instead of simply applying classical seasonal decomposition. We empirically evaluate the new proposition in the M3-Competition data with very promising results in terms of forecast accuracy.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability, Time-Series & Forecasting

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Citations in the corpus

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019, methodology): “90 EXP (Spiliotis et al., 2019) 16.”
Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “Syntetos et al. (2010) find out that small gains in forecasting accuracy may lead to considerable inventory reductions, Jakubovskis (2017) stresses the importance of robustness for strategic facility location, capacity acquisition and technology choice decisions, while Ma & Fildes (2017) suggest tha”
Forecasting: theory and practice (2020, methodology): “…Nikolopoulos et al. (2012), the optimised theta models and their relationship with state space models in Fioruci et al. (2015) and Fiorucci et al. (2016), hybrid approaches as in Theodosiou (2011) and Spiliotis et al. (2019a), to the very latest generalised theta method of Spiliotis et al. (2020a).”