【问题标题】:Holt forecast for multiple timeseries in RR中多个时间序列的霍尔特预测
【发布时间】:2020-09-04 14:45:08
【问题描述】:

我正在尝试对多个时间序列进行 Holt 的预测,并将它们与我的原始 data.frame 结合起来。考虑以下 data.frame,其中我有两个人口组:

library("forecast")

d <- data.frame(SEX       = c("MALE","MALE","MALE","FEMALE","FEMALE","FEMALE"),
                EDUCATION = c("01","01","01","01","01","01"),
                TIME      = c("2000","2001","2002","2000","2001","2002"),
                VALUE     = c(120,150,140,90,75,60))

然后我正在对两个时间序列进行 Holt 预测:

male   <- ts(as.numeric(d[1:3,]$VALUE),start=c(2000))
female <- ts(as.numeric(d[4:6,]$VALUE),start=c(2000))

forecastmale   <- holt(male,h = 3,damped = FALSE)
forecastfemale <- holt(female,h = 3,damped = FALSE)

然后我保存结果并与我原来的data.frame结合:

forecastmale   <- data.frame(forecastmale[["mean"]])
forecastfemale <- data.frame(forecastfemale[["mean"]])

forecastmale$SEX            <- c("MALE","MALE","MALE")
forecastmale$EDUCATION      <- c("01","01","01")
forecastmale$TIME           <- c("2003","2004","2005")
colnames(forecastmale)[1]   <- "VALUE" 
forecastmale                <- forecastmale[, c(2,3,4,1)]

forecastfemale$SEX          <- c("FEMALE","FEMALE","FEMALE")
forecastfemale$EDUCATION    <- c("01","01","01")
forecastfemale$TIME         <- c("2003","2004","2005")
colnames(forecastfemale)[1] <- "VALUE"
forecastfemale              <- forecastfemale[, c(2,3,4,1)]

d <- rbind(d,forecastmale,forecastfemale)

这在我只有两个时间序列时有效。但是,如果我有 100 个需要预测的时间序列,那么这不是一种非常有效的方法。任何人都可以帮助提高编码器的效率,例如,如果我在我的 data.frame 中包含一个额外的人口组,那么我没有对代码进行任何更改?

【问题讨论】:

    标签: r forecasting holtwinters


    【解决方案1】:

    这就是 fable 包的设计目的。这是一个使用与您相同的数据结构的示例。

    library(dplyr)
    library(tsibble)
    library(fable)
    
    # Artifical data
    df <- expand.grid(
        education = 1:3,
        sex = c("male","female"),
        year = 1990:2002
      ) %>%
      as_tsibble(index=year, key=c(sex,education)) %>%
      mutate(value = rnorm(78))
    
    # Fit Holt's method to each series and forecast 3 years ahead
    df %>%
      model(holt = ETS(value ~ trend("A"))) %>%
      forecast(h=3)
    #> # A fable: 18 x 6 [1Y]
    #> # Key:     sex, education, .model [6]
    #>    sex    education .model  year         value  .mean
    #>    <fct>      <int> <chr>  <dbl>        <dist>  <dbl>
    #>  1 male           1 holt    2003  N(0.14, 1.7)  0.137
    #>  2 male           1 holt    2004  N(0.17, 1.7)  0.171
    #>  3 male           1 holt    2005  N(0.21, 1.7)  0.205
    #>  4 male           2 holt    2003 N(-0.75, 1.5) -0.749
    #>  5 male           2 holt    2004 N(-0.84, 1.8) -0.837
    #>  6 male           2 holt    2005   N(-0.93, 2) -0.926
    #>  7 male           3 holt    2003  N(0.51, 0.7)  0.514
    #>  8 male           3 holt    2004  N(0.53, 0.7)  0.530
    #>  9 male           3 holt    2005  N(0.55, 0.7)  0.546
    #> 10 female         1 holt    2003 N(0.44, 0.98)  0.445
    #> 11 female         1 holt    2004 N(0.47, 0.98)  0.470
    #> 12 female         1 holt    2005  N(0.5, 0.98)  0.495
    #> 13 female         2 holt    2003 N(0.13, 0.89)  0.127
    #> 14 female         2 holt    2004 N(0.15, 0.89)  0.148
    #> 15 female         2 holt    2005 N(0.17, 0.89)  0.168
    #> 16 female         3 holt    2003  N(0.78, 1.8)  0.781
    #> 17 female         3 holt    2004  N(0.88, 1.8)  0.880
    #> 18 female         3 holt    2005  N(0.98, 1.8)  0.978
    

    reprex package (v0.3.0) 于 2020-09-05 创建

    【讨论】:

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