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我想基于 purrr 和预测包构建许多 auto.arima 模型。我无法完成以下代码，出现一些错误。

如果有必要我可以提供，我们可以在没有可重现代码的情况下开始。

我的数据：

head(df)
nam period     sv
APA 2016-07-03 1895619
APA 2016-07-10 2100690
APA 2016-07-17 2059273
APA 2016-07-24 2073187
APA 2016-07-31 1951968

我在R中的代码要完成......

df %>% 
nest(-nam) %>% 
mutate(ts_data = map(data, tk_ts, select = sv, start = c(2016,26), frequency = 52)) %>% 
mutate(harmonics = map(ts_data, fourier, K=24)) %>% 
mutate(fitted = map2(.x = ts_data, .y =harmonics, .f= auto.arima, xreg , seasonal = F))

我想实现相当于这段代码：

harmonics <- fourier(db, K = 24)
# Fit regression model with ARIMA errors
fit <- auto.arima(db, xreg = harmonics, seasonal = F)
# Forecasts next 46 periods
newharmonics <- fourier(db, K = 24, h = 46)
fc <- forecast(fit, xreg = newharmonics )

有人可以帮我完成吗？谢谢提前

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1 回答 1

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我解决了。只需将其放入公式中就足够了

`auto_arima = function(df) {
harmonics <- fourier(df, K = 24)
# Fit regression model with ARIMA errors
fit <- auto.arima(df, xreg = harmonics, seasonal = FALSE)
# Forecasts next 46 period
newharmonics <- fourier(df, K = 24, h = 46)
fc <- forecast(fit, xreg = newharmonics)
fc_db = fc %>% as_data_frame() %>% select(`Point Forecast`) %>% 
mutate(period = seq.Date(as.Date("2017-10-15"), as.Date("2018-08-27"),by = "week"))
return(fc_db)
}

` 然后：

mutate(fitted = map(ts_data, auto_arima))

塞维

于 2018-08-30T14:15:15.637 回答