论文标题
STR：使用回归的季节性趋势分解
STR: Seasonal-Trend Decomposition Using Regression
论文作者
论文摘要
我们提出了一种分解季节性数据的新方法：STR（使用回归的季节性趋势分解）。与其他分解方法不同，STR允许使用多个季节性和循环成分，协变量，可能具有非整体时期的季节性模式以及具有复杂拓扑的季节性。它可用于时间序列，其中包括任何常规时间指数，包括每小时，每周，每月或季度数据。当现有方法存在时，它具有竞争力，但要解决比其他方法允许的分解问题更多。 STR基于正规化优化，因此与脊回归有关。因为它基于统计模型，所以我们可以轻松地计算组件的置信区间，而大多数现有的分解方法（例如STL，X-12-Arima，Seats-Tramo等）是不可能的。 我们的模型在R软件包中实现，因此任何人都可以应用于自己的数据。
We propose a new method for decomposing seasonal data: STR (a Seasonal-Trend decomposition using Regression). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have non-integer periods, and seasonality with complex topology. It can be used for time series with any regular time index including hourly, daily, weekly, monthly or quarterly data. It is competitive with existing methods when they exist, but tackles many more decomposition problem than other methods allow. STR is based on a regularized optimization, and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as STL, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR, so can be applied by anyone to their own data.
