用于每日数据预测的 auto.arima 日期太远

Question

I have daily data of visitors, which I'm trying to forecast using auto.arima. The problem is that the dataset ends in 2017-09-10, but the date for the first forecast is in 2025. I would like the model to make forecasts h days into the future, but it starts at the wrong date and makes just 7 forecasts a year instead of 365/366. This probably has something to do with the tsibble data structure and its handling of dates into the arima model but I'm not sure.

The actual dataset is longer but I'm using the shorter one just as an example.

library(forecast)
library(tsibble)

data <- structure(list(dates = structure(c(17366, 17367, 17368, 17369, 
17370, 17371, 17372, 17373, 17374, 17375, 17376, 17377, 17378, 
17379, 17380, 17381, 17382, 17383, 17384, 17385, 17386, 17387, 
17388, 17389, 17390, 17391, 17392, 17393, 17394, 17395, 17396, 
17397, 17398, 17399, 17400, 17401, 17402, 17403, 17404, 17405, 
17406, 17407, 17408, 17409, 17410, 17411, 17412, 17413, 17414, 
17415, 17416, 17417, 17418, 17419), class = "Date"), amount = c(140259004L, 
137461014L, 133577835L, 140119981L, 150459411L, 150351610L, 146260160L, 
140679789L, 137475996L, 132494397L, 136308902L, 147320206L, 150067135L, 
140510359L, 139777366L, 136165099L, 131913565L, 131895017L, 143034246L, 
149088594L, 146601589L, 146642062L, 143600939L, 135980097L, 141922119L, 
148676920L, 152191991L, 157564268L, 153750311L, 147384628L, 138167523L, 
136748018L, 147513392L, 152316844L, 146654846L, 147868709L, 140309766L, 
137225882L, 139028747L, 155939179L, 160846148L, 153346249L, 147921236L, 
148184826L, 146683058L, 144881045L, 166062400L, 166791506L, 162190588L, 
172354146L, 180731284L, 136754670L, 132359512L, 141863949L)), row.names = c(NA, 
-54L), class = "data.frame")

data <- as_tsibble(data)

tail(data$dates)

auto.arima(data) %>% forecast(10)

Answer 1

You are mixing packages that should not go together. The forecast package handles ts objects not tsibble objects. Replace the forecast package with the fable package when using tsibble objects.

library(tsibble)
library(fable)

data <- tsibble(
  dates = structure(c(
    17366, 17367, 17368, 17369, 17370, 17371, 17372, 17373, 17374,
    17375, 17376, 17377, 17378, 17379, 17380, 17381, 17382, 17383,
    17384, 17385, 17386, 17387, 17388, 17389, 17390, 17391, 17392,
    17393, 17394, 17395, 17396, 17397, 17398, 17399, 17400, 17401,
    17402, 17403, 17404, 17405, 17406, 17407, 17408, 17409, 17410,
    17411, 17412, 17413, 17414, 17415, 17416, 17417, 17418, 17419
  ), class = "Date"),
  amount = c(
    140259004L,
    137461014L, 133577835L, 140119981L, 150459411L, 150351610L, 146260160L,
    140679789L, 137475996L, 132494397L, 136308902L, 147320206L, 150067135L,
    140510359L, 139777366L, 136165099L, 131913565L, 131895017L, 143034246L,
    149088594L, 146601589L, 146642062L, 143600939L, 135980097L, 141922119L,
    148676920L, 152191991L, 157564268L, 153750311L, 147384628L, 138167523L,
    136748018L, 147513392L, 152316844L, 146654846L, 147868709L, 140309766L,
    137225882L, 139028747L, 155939179L, 160846148L, 153346249L, 147921236L,
    148184826L, 146683058L, 144881045L, 166062400L, 166791506L, 162190588L,
    172354146L, 180731284L, 136754670L, 132359512L, 141863949L
  ),
  index = dates
)

data %>%
  model(
    arima = ARIMA(amount)
  ) %>%
  forecast(h = 10)
#> # A fable: 10 x 4 [1D]
#> # Key:     .model [1]
#>    .model dates          amount .distribution      
#>    <chr>  <date>          <dbl> <dist>             
#>  1 arima  2017-09-11 156148449. N(1.6e+08, 4.6e+13)
#>  2 arima  2017-09-12 156677612. N(1.6e+08, 6.5e+13)
#>  3 arima  2017-09-13 160812213. N(1.6e+08, 6.8e+13)
#>  4 arima  2017-09-14 160909814. N(1.6e+08, 6.8e+13)
#>  5 arima  2017-09-15 144449326. N(1.4e+08, 6.8e+13)
#>  6 arima  2017-09-16 143739717. N(1.4e+08, 6.8e+13)
#>  7 arima  2017-09-17 156979719. N(1.6e+08, 6.8e+13)
#>  8 arima  2017-09-18 163510495. N(1.6e+08, 7.3e+13)
#>  9 arima  2017-09-19 160618600. N(1.6e+08, 7.4e+13)
#> 10 arima  2017-09-20 162818145. N(1.6e+08, 7.5e+13)

^{Created on 2020-04-01 by the reprex package (v0.3.0)}