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

问题描述

我们每个月必须拟合大约 2000 个或奇数的时间序列，特别是它们具有非常特殊的行为，有些是 arma/arima，有些是 ewma，有些是 arch/garch，有或没有季节性和/或趋势(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).

理论上可以使用 aic 或 bic 标准构建集成模型来选择最佳拟合模型，但社区是否知道任何试图解决此问题的库?

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 让我知道了 Rob J Hyndman 的以下内容链接

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

但它们还有其他选择吗?

but are they any other alternatives?

推荐答案

forecast 包中有两个自动方法:auto.arima() 将使用 ARIMA 模型处理自动建模，ets() 将自动从指数平滑系列中选择最佳模型(包括适当的趋势和季节性).AIC 在这两种情况下都用于模型选择.不过，两者都不能处理 ARCH/GARCH 模型.此 JSS 文章中详细描述了该软件包: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

进一步回答您的问题:

什么时候可以使用预测包功能，尤其是ets函数，高维数据(例如每周数据)?

大概明年初吧.论文已经写好(参见 robjhyndman.com/working-papers/complex-seasonality)，我们现在正在编写代码.

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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