论文标题
预测多个季节性
Forecasting with Multiple Seasonality
论文作者
论文摘要
新兴的现代应用程序涉及预测时间序列数据，这些数据表现出短时动态和长期季节性。具体而言，具有多个季节性的时间序列是一项艰巨的任务，讨论相对较少。在本文中，我们提出了一种具有多个季节性的时间序列的两阶段方法，这不需要预定的季节性。在第一阶段，我们将经典的季节性自动回归移动平均平均值（ARMA）模型概括为多个季节性制度。在第二阶段，我们利用适当的标准进行滞后顺序选择。模拟和实证研究表明，我们方法的出色预测性能，尤其是与最近流行的“ Facebook先知”模型相比。
An emerging number of modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. Specifically, time series with multiple seasonality is a difficult task with comparatively fewer discussions. In this paper, we propose a two-stage method for time series with multiple seasonality, which does not require pre-determined seasonality periods. In the first stage, we generalize the classical seasonal autoregressive moving average (ARMA) model in multiple seasonality regime. In the second stage, we utilize an appropriate criterion for lag order selection. Simulation and empirical studies show the excellent predictive performance of our method, especially compared to a recently popular `Facebook Prophet' model for time series.
