论文标题
轨道：概率预测，指数平滑
Orbit: Probabilistic Forecast with Exponential Smoothing
论文作者
论文摘要
时间序列预测是学术界和行业中的一个积极研究主题。尽管我们看到，在解决这些预测挑战中的某些挑战方面，机器学习方法的采用量越来越多，但统计方法在处理低粒度数据的同时仍然有力。本文借助包括Stan在内的概率编程语言介绍了精致的贝叶斯指数平滑模型。我们的模型改进包括其他全球趋势，乘法形式的转换，噪声分布和先验的选择。对我们模型的丰富时间序列数据集以及其他知名的时间序列模型进行了基准研究。
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.
