使用寓言包，预测风扇不会在实际行的末尾开始

Question

我已使用R中的fable包构建了以下的扇形图。我想知道是否有人对我的预测扇形的起源点不是来自实际线（起点的外部点）提出了建议距离实际线还远吗？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内容

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')

编辑：我正在寻找一种解决方案，使不确定性演变的外部带（预测扇）在开始时更窄，并且随着时间的推移逐渐散开，类似于我附加的英格兰银行图下面

Answer 1

show_gap的{​​{1}}选项要求通过autoplot(<fable>)提供历史数据。

autoplot(<fable>, <tsibble>, show_gap = FALSE)

^{由reprex package（v0.3.0）于2020-09-23创建}

如果需要制作更自定义的图形，建议不要使用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') 并使用ggplot2编写自己的图形。

要将图形形式的预测与数据结合起来，可以在寓言中添加另一行，这是对数据的最后观察：

autoplot()

^{由reprex package（v0.3.0）于2020-09-23创建}

从那里，您可以使用{ggdist}包来可视化分布，并使用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. 添加历史数据。