@misc{10481/97766,
year = {2024},
month = {11},
url = {https://hdl.handle.net/10481/97766},
abstract = {This paper describes a family of seasonal and non-seasonal time series models that
can be viewed as generalisations of additive and multiplicative exponential smoothing
models to model series that grow faster than linear but slower than exponential.
Their development is motivated by fast-growing, volatile time series. In particular, our
models have a global trend that can smoothly change from additive to multiplicative
and is combined with a linear local trend. Seasonality, when used, is multiplicative
in our models, and the error is always additive but heteroscedastic and can grow
through a parameter sigma. We leverage state-of-the-art Bayesian fitting techniques
to fit these models accurately, which are more complex and flexible than standard
exponential smoothing models. When applied to the M3 competition data set, our
models outperform the best algorithms in the competition and other benchmarks, thus
achieving, to the best of our knowledge, the best results of per-series univariate methods
on this dataset in the literature. An open-source software package of our method is
available.},
organization = {María Zambrano
(Senior) Fellowship that is funded by the Spanish Ministry
of Universities},
organization = {Next Generation funds from the European
Union},
organization = {Australian
Research Council under grant DE190100045},
publisher = {El Sevier},
keywords = {Exponential Smoothing},
keywords = {Bayesian Modelling},
keywords = {Time Series Forecasting},
title = {Local and global trend Bayesian exponential smoothing models},
doi = {10.1016/j.ijforecast.2024.03.006},
author = {Smyl, Slawek and Bergmeir, Christoph Norbert and Dokumentov, Alexander and Long, Xueying and Wibowo, Erwin and F. Schmidt, Daniel},
}