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R 中的寓言 package 中的自动 ETS 不提供具有最低信息标准(AIC、BIC 和 AICc)的 model

[英]Automatic ETS from fable package in R does not provide the model with the lowest Information Criteria (AIC, BIC and AICc)

Using the classic shampoo dataset from fpp2 I tried to fit an automatic ets model.使用来自 fpp2 的经典洗发水数据集,我尝试安装自动 ets model。 This led me to an ETS(A,A,N) framework with aic = 441.0668, bic = 448.9844 and AICc = 443.0668.这使我找到了一个 ETS(A,A,N) 框架,其中 aic = 441.0668、bic = 448.9844 和 AICc = 443.0668。 Nevertheless when using an ETS(A,M,N) model the information criteria metrics are lower (AIC=440.1991, BIC=448.1167, AICc=442.1991), even when the parameter Restrict equals false (on the automatic ets).然而,当使用 ETS(A,M,N) model 时,信息标准指标较低(AIC=440.1991,BIC=448.1167,AICc=442.1991),即使参数 Restrict 等于 false(在自动 ets 上)。

'''automatic_fit <- ts_train %>% model(ETS(value, restrict= FALSE)) '''automatic_fit <- ts_train %>% model(ETS(value, restrict= FALSE))

report(automatic_fit)报告(自动适应)

fit_2 <- ts_train %>% model(ETS(value ~ error("A") + trend("M"), restrict = FALSE)) fit_2 <- ts_train %>% 模型(ETS(值 ~ 错误(“A”)+ 趋势(“M”),限制 = FALSE))

report(fit_2)'''报告(fit_2)'''

I know that with the autoarima we have an approximation of the best model since there are to many to check, but I thought that since with ets we have a small number of possibilities we could evaluate all the models.我知道使用 autoarima 我们有一个最好的 model 的近似值,因为有很多要检查的,但我认为由于使用 ets 我们有少量的可能性,我们可以评估所有模型。 Does anyone knows why we see this results?有谁知道为什么我们会看到这个结果?

By default, the automatic modelling selection done by fable::ETS does not consider multiplicative trend models.默认情况下,由fable::ETS完成的自动建模选择不考虑乘法趋势模型。 The models considered in the selection process are described by the method argument of the model's specials.选择过程中考虑的模型由模型的特价商品的method参数描述。 The default for the trend() special is trend(method = c("N", "A", "Ad")) , meaning it will choose the best model between models without trend, with additive trend, and with additive damped trend. Trend trend()特殊的默认值是trend(method = c("N", "A", "Ad")) ,这意味着它将在没有趋势的模型之间选择最好的model,有加性趋势,有加性阻尼趋势. To also consider multiplicative trend, you would use trend(method = c("N", "A", "Ad", "M")) .要考虑乘法趋势,您可以使用trend(method = c("N", "A", "Ad", "M"))

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