问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？
EN

Stack Overflow用户

提问于 2021-07-02 12:06:20

回答 1查看 35关注 0票数 1

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"))

loops

data-manipulation

forecast

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回答 1

Stack Overflow用户

回答已采纳

发布于 2021-07-02 12:10:15

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" 
...