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我已经使用 R 中的包构建了以下扇形图fable。我想知道是否有人对为什么我的预测风扇的原点与实际线不符有任何建议（开头的外部点与实际线相距甚远） ? 这是建模错误还是我无法避免的数据问题？

这是我的数据集的可复制品

structure(list(Date = structure(c(12418, 12509, 12600, 12692, 
12784, 12874, 12965, 13057, 13149, 13239, 13330, 13422, 13514, 
13604, 13695, 13787, 13879, 13970, 14061, 14153, 14245, 14335, 
14426, 14518, 14610, 14700, 14791, 14883, 14975, 15065, 15156, 
15248, 15340, 15431, 15522, 15614, 15706, 15796, 15887, 15979, 
16071, 16161, 16252, 16344, 16436, 16526, 16617, 16709, 16801, 
16892, 16983, 17075, 17167, 17257, 17348, 17440, 17532, 17622, 
17713, 17805), fiscal_start = 1, class = c("yearquarter", "vctrs_vctr"
)), Index = c(99.9820253708305, 100.194245830908, 100.464139353185, 
100.509664967831, 100.0275008635, 100.372695892486, 100.468066533557, 
100.576244163805, 100.623717628381, 100.780442246863, 100.65264776914, 
100.69366042058, 100.909079987983, 101.018619794549, 100.959015810121, 
101.04835942569, 100.681089538573, 100.663660573108, 100.522268447626, 
100.22783149065, 99.4643787364223, 99.4331456182866, 99.5626187912313, 
100.039081681562, 100.418818090577, 100.4652077117, 100.544938523663, 
100.643407515773, 100.44741458842, 100.502455228311, 100.695097023592, 
100.716907300461, 100.555884307168, 100.503742436422, 100.432566888692, 
100.553320081068, 100.32442656222, 100.456727368091, 100.350509427919, 
100.677833560057, 100.362403841025, 100.827860652847, 100.499496900756, 
100.418652455482, 100.234221207155, 100.25208930362, 100.159571677823, 
100.229735300634, 100.369332695161, 100.169972399177, 100.17207717391, 
100.35130514679, 99.9317959389533, 99.8704136030018, 100.052802025981, 
100.176345514426, 100.355049154025, 100.544145324359, 100.549886876118, 
100.5559420697)), row.names = c(NA, -60L), key = structure(list(
    .rows = structure(list(1:60), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -1L), class = c("tbl_df", 
"tbl", "data.frame")), index = structure("Date", ordered = TRUE), index2 = "Date", interval = structure(list(
    year = 0, quarter = 1, month = 0, week = 0, day = 0, hour = 0, 
    minute = 0, second = 0, millisecond = 0, microsecond = 0, 
    nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", 
"vctrs_rcrd", "vctrs_vctr")), class = c("tbl_ts", "tbl_df", "tbl", 
"data.frame"))

和我的代码


fit <- afsi %>%
  model(arima = ARIMA(log(Index)))

p <- fit %>%
  forecast(h="2 year") %>%
  autoplot(bind_rows(afsi %>% slice(tail(row_number(), 12)), select(slice(., 1), Date, Index = .mean)), level=seq(10,90,by=10), show_gap = TRUE) +
  geom_line(aes(Date,Index), col = '#75002B', size=1.2) +
  theme_bw() +
  labs(y='Log (AFSI)', title = 'Fanchart - Aggregate Financial Stability Index',
       subtitle = '8 period forecast (2019Q1-2020Q4)') 

  

p$layers[[1]]$aes_params$fill <- "#75002B"

p + theme(legend.position = 'none')

在此处输入图像描述

编辑：我正在寻找一种解决方案，使我的不确定性演变中的外部带（预测扇形）在开始时更窄，并且随着时间的推移它们呈扇形散开，类似于我在下面附上的英格兰银行图

在此处输入图像描述

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1 回答 1

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show_gap选项autoplot(<fable>)要求通过 提供历史数据autoplot(<fable>, <tsibble>, show_gap = FALSE)。

library(fable)
library(dplyr)
library(ggplot2)
fit <- afsi %>%
  model(arima = ARIMA(log(Index)))

fit %>%
  forecast(h="2 year") %>%
  autoplot(tail(afsi, 12), level=seq(10,90,by=10), show_gap = FALSE) +
  theme_bw() +
  labs(y='Log (AFSI)', title = 'Fanchart - Aggregate Financial Stability Index',
       subtitle = '8 period forecast (2019Q1-2020Q4)') +
  theme(legend.position = 'none')

由reprex 包（v0.3.0）于 2020 年 9 月 23 日创建

如果您需要生成更自定义的图形，我建议您不要使用autoplot()ggplot2 并编写自己的绘图。

要将预测以图形方式连接到数据，您可以在寓言中添加另一行，这是对数据的最后一次观察：

fc <- fit %>% 
  forecast(h = "2 years")

fc_no_gap <- afsi %>% 
  tail(1) %>% 
  # Match structure of fable to combine with
  mutate(.model = "arima", Index = distributional::dist_degenerate(Index), .mean = mean(Index)) %>% 
  as_fable(distribution = Index, response = "Index") %>% 
  bind_rows(fc)
#> Warning: The dimnames of the fable's distribution are missing and have been set
#> to match the response variables.

由reprex 包（v0.3.0）于 2020 年 9 月 23 日创建

从那里你可以使用{ggdist}包来可视化分布，并geom_line()添加历史数据。

于 2020-09-23T02:41:12.053 回答