Orbit: Probabilistic Forecast with Exponential Smoothing
Abstract
Time series forecasting is an active research topic in academia as well as industry. Although we see an increasing amount of adoptions of machine learning methods in solving some of those forecasting challenges, statistical methods remain powerful while dealing with low granularity data. This paper introduces a refined Bayesian exponential smoothing model with the help of probabilistic programming languages including Stan. Our model refinements include additional global trend, transformation for multiplicative form, noise distribution and choice of priors. A benchmark study is conducted on a rich set of time-series data sets for our models along with other well-known time series models.
Cite
@article{arxiv.2004.08492,
title = {Orbit: Probabilistic Forecast with Exponential Smoothing},
author = {Edwin Ng and Zhishi Wang and Huigang Chen and Steve Yang and Slawek Smyl},
journal= {arXiv preprint arXiv:2004.08492},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:1909.13316 by other authors