如何在我的环境中自动创建更多的模型，同时更改3个参数？
如何在我的环境中自动创建更多的模型，同时更改3个参数？
提问于 2021-07-02 20:06:20
一个完全可复制的例子。
library(forecast)
date = seq(as.Date("2019/01/01"), by = "month", length.out = 48)
productB = rep("B",48)
productB = rep("B",48)
productA = rep("A",48)
productA = rep("A",48)
subproducts1=rep("1",48)
subproducts2=rep("2",48)
subproductsx=rep("x",48)
subproductsy=rep("y",48)
b1 <- c(rnorm(30,5), rep(0,18))
b2 <- c(rnorm(30,5), rep(0,18))
b3 <-c(rnorm(30,5), rep(0,18))
b4 <- c(rnorm(30,5), rep(0,18))创建了下面的数据
dfone <- data.frame("date"= rep(date,4),
"product"= c(rep(productB,2),rep(productA,2)),
"subproduct"=
c(subproducts1,subproducts2,subproductsx,subproductsy),
"actuals"= c(b1,b2,b3,b4))
export_df <- split(dfone[1:4], dfone[3])基于独特子产品的数据帧的创建
dummy_list <- split(dfone[1:4], dfone[3]) %>% lapply( function(x)
x[(names(x) %in% c("date", "actuals"))])
dummy_list <- lapply(dummy_list, function(x) { x["date"] <- NULL; x })
list_dfs <- list()
for (i in 1:length(unique(dfone$subproduct))) {
#assign(paste0("df", i), as.data.frame(dummy_list[[i]]))
list_dfs <-append(list_dfs,dummy_list[[i]])
}
combined_dfs <- Reduce(function(x, y) merge(x, y, all = TRUE,
by='date'), list(list_dfs))创建时间序列
list_ts <- lapply(list_dfs, function(t)
ts(t,start=c(2019,1),end=c(2021,6), frequency = 12)) %>%
lapply( function(t) ts_split(t,sample.out=(0.2*length(t)))) #
creates my train test split
list_ts <- do.call("rbind", list_ts) #Creates a list of time series有个问题。这不会给我超过9种型号。例如，我想要一个n1 =.1 n2=.99和n3= .3的模型，这样我们就有了9种以上的模型。
n1 <- seq(0.1, 0.99, by = 0.1)
n2 <- seq(0.1, 0.99, by = 0.1)
n3 <- seq(0.1, 0.99, by = 0.1)
out<- lapply(seq_along(n1), function(i) {
cw_triple_holtwinters_additive <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(ses(x,h=24,alpha =
n1[i],beta=n2[i],gamma=n3[i])))
cw_triple_holtwinters_additive <-
lapply(cw_triple_holtwinters_additive, "[", "mean")
assign(paste0("cw_triple_holtwinters_additive", i),
cw_triple_holtwinters_additive, envir = .GlobalEnv)
cw_triple_holtwinters_additive})附加问题:对于order=c(1,1,1)和order=c(0,1,0)，我是否可以创建类似这些值的列表，并同时遍历它们，就像Akrun的解决方案一样？
cw_seasonal_autoregressive_integratedmovingaverage1 <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(1,1,1),seasonal=list(order=c(0,1,0),
period=12)) ,h=24))
cw_seasonal_autoregressive_integratedmovingaverage1 <-
lapply(cw_seasonal_autoregressive_integratedmovingaverage1, "[",
c("mean"))回答 1
Stack Overflow用户
回答已采纳
发布于 2021-07-02 20:10:15
我们可以使用expand.grid来获得所有的组合
dat_n <- expand.grid(n1 = n1, n2= n2, n3 = n3) 然后，我们遍历“dat_n”的行序列。
out<- lapply(seq_len(nrow(dat_n)), function(i) {
cw_triple_holtwinters_additive <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(ses(x,h=24,alpha =
dat_n$n1[i],beta=dat_n$n2[i],gamma=dat_n$n3[i])))
cw_triple_holtwinters_additive <-
lapply(cw_triple_holtwinters_additive, "[", "mean")
assign(paste0("cw_triple_holtwinters_additive", i),
cw_triple_holtwinters_additive, envir = .GlobalEnv)
cw_triple_holtwinters_additive})-checking
ls(pattern = "cw_triple")
[1] "cw_triple_holtwinters_additive1" "cw_triple_holtwinters_additive10" "cw_triple_holtwinters_additive100" "cw_triple_holtwinters_additive101"
[5] "cw_triple_holtwinters_additive102" "cw_triple_holtwinters_additive103" "cw_triple_holtwinters_additive104" "cw_triple_holtwinters_additive105"
[9] "cw_triple_holtwinters_additive106" "cw_triple_holtwinters_additive107" "cw_triple_holtwinters_additive108" "cw_triple_holtwinters_additive109"
[13] "cw_triple_holtwinters_additive11" "cw_triple_holtwinters_additive110" "cw_triple_holtwinters_additive111" "cw_triple_holtwinters_additive112"
[17] "cw_triple_holtwinters_additive113" "cw_triple_holtwinters_additive114" "cw_triple_holtwinters_additive115" "cw_triple_holtwinters_additive116"
[21] "cw_triple_holtwinters_additive117"
...页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/68230752
复制相关文章
相似问题
腾讯云开发者
