关于使用R自动进行时间序列拟合的问题 [英] On the issue of automatic time series fitting using R

问题描述

我们每个月必须适应大约2000个或奇数个时间序列，特别是
它们具有非常特有的行为，有些是arma / arima，有些是ewma，有些是具有或没有季节性和/或没有的arch / garch趋势（唯一的共同点是时间序列方面）。

we have to fit about 2000 or odd time series every month, they have very idiosyncratic behavior in particular, some are arma/arima, some are ewma, some are arch/garch with or without seasonality and/or trend (only thing in common is the time series aspect).

从理论上讲，一个可以建立带有aic或bic准则的集成模型，以选择最佳拟合模型，但它是社区

one can in theory build ensemble model with aic or bic criterion to choose the best fit model but is the community aware of any library which attempts to solve this problem?

Google让我知道Rob J Hyndman的以下图书馆
链接

Google made me aware of the below one by Rob J Hyndman link

但是它们还有其他选择吗？

but are they any other alternatives?

推荐答案

在预测包： auto.arima（），它将使用ARIMA模型和 ets（）处理自动建模，它将自动从指数平滑族中选择最佳模型（适当时包括趋势和季节性）。在两种情况下都使用AIC进行模型选择。两者都不处理ARCH / GARCH模型。该JSS文章中对该软件包进行了详细说明： http://www.jstatsoft.org/v27/ i03

There are two automatic methods in the forecast package: auto.arima() which will handle automatic modelling using ARIMA models, and ets() which will automatically select the best model from the exponential smoothing family (including trend and seasonality where appropriate). The AIC is used in both cases for model selection. Neither handles ARCH/GARCH models though. The package is described in some detail in this JSS article: http://www.jstatsoft.org/v27/i03

进一步的问题：

何时可以使用具有高维
数据（例如每周数据）的
预测软件包功能，尤其是
ets函数？

可能在明年年初。本文已撰写（请参见robjhyndman.com/working-papers/complex-seasonality），我们现在正在研究代码。

Probably early next year. The paper is written (see robjhyndman.com/working-papers/complex-seasonality) and we are working on the code now.

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