如何让 highcharter 代表预测对象？
How can I get highcharter to represent a forecast object?
这是 的后续。
我正在尝试让该问题中给出的管道接受预测对象作为输入：
再次使用此数据：
> dput(t)
structure(c(2, 2, 267822980, 325286564, 66697091, 239352431,
94380295, 1, 126621669, 158555699, 32951026, 23, 108000151, 132505189,
29587564, 120381505, 25106680, 117506099, 22868767, 115940080,
22878163, 119286731, 22881061), .Dim = c(23L, 1L), index = structure(c(1490990400,
1490994000, 1490997600, 1491001200, 1491004800, 1491008400, 1491012000,
1491026400, 1491033600, 1491037200, 1491040800, 1491058800, 1491062400,
1491066000, 1491069600, 1491073200, 1491076800, 1491109200, 1491112800,
1491120000, 1491123600, 1491156000, 1491159600), tzone = "US/Mountain", tclass = c("POSIXct",
"POSIXt")), class = c("xts", "zoo"), .indexCLASS = c("POSIXct",
"POSIXt"), tclass = c("POSIXct", "POSIXt"), .indexTZ = "US/Mountain", tzone = "US/Mountain", .CLASS = "double", .Dimnames = list(
NULL, "count"))
我用
highchart(type = 'stock') %>%
hc_add_series(t) %>%
hc_xAxis(type = 'datetime')
创建
但是如果我按照同样的食谱使用
require("forecast")
t.arima <- auto.arima(t)
x <- forecast(t.arima, level = c(95, 80))
highchart(type = 'stock') %>%
hc_add_series(x) %>%
hc_xAxis(type = 'datetime')
我收到这个错误：
Error in as.Date.ts(.) : unable to convert ts time to Date class
如何将预测系列与历史系列一起显示？我在文档中看到过这个，但不明白为什么我会收到这个错误。
JK 的 JS 控制台输出：
重新索引后的 DF 数据：
dput(df)
structure(list(Index = structure(c(1490968800, 1490972400, 1490976000,
1490979600, 1490983200, 1490986800, 1490990400, 1491004800, 1491012000,
1491015600, 1491019200, 1491037200, 1491040800, 1491044400, 1491048000,
1491051600, 1491055200, 1491087600, 1491091200, 1491098400, 1491102000,
1491134400, 1491138000, 1491217200, 1491220800, 1491224400, 1491228000,
1491231600, 1491235200, 1491238800, 1491242400, 1491246000, 1491249600,
1491253200, 1491256800, 1491260400, 1491264000, 1491267600), class = c("POSIXct",
"POSIXt")), Data = c(2, 2, 259465771, 315866206, 64582553, 233440220,
91918347, 1, 126563786, 158555699, 32951026, 23, 108000151, 132505189,
29587564, 120381505, 25106680, 117506099, 22868767, 115898351,
22878163, 119285747, 22881061, 157925588, 32447780, 223096830,
281656273, 45406684, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA),
Fitted = c(102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA),
`Point Forecast` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143), `Lo 80` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -16003477.5789723,
-16003477.5789723, -16003477.5789723, -16003477.5789723,
-16003477.5789723, -16003477.5789723, -16003477.5789723,
-16003477.5789723, -16003477.5789723, -16003477.5789723),
`Hi 80` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 220344625.293258, 220344625.293258, 220344625.293258,
220344625.293258, 220344625.293258, 220344625.293258, 220344625.293258,
220344625.293258, 220344625.293258, 220344625.293258), `Lo 95` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -78561041.5917782,
-78561041.5917782, -78561041.5917782, -78561041.5917782,
-78561041.5917782, -78561041.5917782, -78561041.5917782,
-78561041.5917782, -78561041.5917782, -78561041.5917782),
`Hi 95` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 282902189.306064, 282902189.306064, 282902189.306064,
282902189.306064, 282902189.306064, 282902189.306064, 282902189.306064,
282902189.306064, 282902189.306064, 282902189.306064)), .Names = c("Index",
"Data", "Fitted", "Point Forecast", "Lo 80", "Hi 80", "Lo 95",
"Hi 95"), row.names = c(NA, -38L), class = "data.frame")
不确定这是因为时间序列不规则。
总之，ggfortify:::fortify.forecast是你的朋友。为什么？因为 fortify（尝试）转换数据框中的所有 R 对象。所以：
library(highcharter)
library(forecast)
t.arima <- auto.arima(t)
x <- forecast(t, level = c(95, 80))
library(highcharter)
library(ggplot2)
library(ggfortify)
#>
#> Attaching package: 'ggfortify'
#> The following object is masked from 'package:forecast':
#>
#> gglagplot
class(x)
#> [1] "forecast"
df <- fortify(x)
head(df)
#> Index Data Fitted Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
#> 1 1 2 140658844 NA NA NA NA NA
#> 2 3601 2 121734145 NA NA NA NA NA
#> 3 7201 267822980 105355638 NA NA NA NA NA
#> 4 10801 325286564 127214522 NA NA NA NA NA
#> 5 14401 66697091 153863779 NA NA NA NA NA
#> 6 18001 239352431 142136089 NA NA NA NA NA
现在您可以：
highchart(type = "stock") %>%
hc_add_series(df, "line", hcaes(Index, Data), name = "Original") %>%
hc_add_series(df, "line", hcaes(Index, Fitted), name = "Fitted") %>%
hc_add_series(df, "line", hcaes(Index, `Point Forecast`), name = "Forecast") %>%
hc_add_series(df, "arearange", hcaes(Index, low = `Lo 80`, high = `Hi 80`), name = "Interval")
如您所见，fortify也检测不到实时。所以你需要在你想要的时候转换索引。
错误
Error in as.Date.ts(.) : unable to convert ts time to Date class
是因为你有一个频率未被函数覆盖的ts对象as.Date.ts(.)。当我们看到这个函数做了什么时，这就是我们得到的：
function (x, offset = 0, ...)
{
time.x <- unclass(time(x)) + offset
if (frequency(x) == 1)
as.Date(paste(time.x, 1, 1, sep = "-"))
else if (frequency(x) == 4)
as.Date(paste((time.x + 0.001)%/%1, 3 * (cycle(x) - 1) +
1, 1, sep = "-"))
else if (frequency(x) == 12)
as.Date(paste((time.x + 0.001)%/%1, cycle(x), 1, sep = "-"))
else stop("unable to convert ts time to Date class")
}
此函数仅考虑 ts 对象的频率的 3 个值：1、4 或 12。当我们查看您的对象的频率 x 时，我们发现它 frequency = 0.000277777777777778，所以当 highcharter 使用 x 中的 ts 对象调用函数时，它会停止并给你那个错误。
关于如何 "fix" 我们有两个选择：
- 用
frequency = 1before运行auto.arima将t转换为ts对象（而不是xts对象）和forecast;
- 经过运行
auto.arima和forecast之后，我们可以为未来的日期创建一个索引，并转换[=中的ts对象15=] 到具有正确索引的 xts 对象中。
我说 "fix" 因为这些解决方案并不完美，我们将看到。
选项 1
t <- structure(
c(2, 2, 267822980, 325286564, 66697091, 239352431,
94380295, 1, 126621669, 158555699, 32951026, 23,
108000151, 132505189, 29587564, 120381505, 25106680,
117506099, 22868767, 115940080, 22878163, 119286731,
22881061),
.Dim = c(23L, 1L),
index = structure(c(1490990400, 1490994000, 1490997600,
1491001200, 1491004800, 1491008400,
1491012000, 1491026400, 1491033600,
1491037200, 1491040800, 1491058800,
1491062400, 1491066000, 1491069600,
1491073200, 1491076800, 1491109200,
1491112800, 1491120000, 1491123600,
1491156000, 1491159600),
tzone = "US/Mountain",
tclass = c("POSIXct","POSIXt")),
class = c("xts", "zoo"),
.indexCLASS = c("POSIXct","POSIXt"),
tclass = c("POSIXct", "POSIXt"),
.indexTZ = "US/Mountain",
tzone = "US/Mountain",
.CLASS = "double",
.Dimnames = list(NULL, "count"))
require("forecast")
library(highcharter)
# SOLUTION 1
t.tmp <- ts(t, start=1, end = length(t))
t.arima.1 <- auto.arima(t.tmp)
x.1 <- forecast(t.arima.1, level = c(95, 80))
highchart(type = 'stock') %>%
hc_add_series(x.1) %>%
hc_add_series(x.1$x, name = "Original") %>%
hc_add_series(x.1$fitted, name = "Fitted")
这种方法的问题是我们丢失了日期（轴、工具提示等）。
选项 2，第一次尝试：每小时预报
我试图为未来值创建一个小时索引，但出于某种原因，Highcharter 将间隔向左移动（或者日期存在一些问题，我无法 see/figure 出来）。
选项 2，第二次尝试：每日预报
当我将其更改为未来值的每日指数时，它起作用了，但这很奇怪，因为我们有每小时的观察结果，并且我们的图表的预测部分显示 "daily forecasts"。
完整代码如下：
t <- structure(
c(2, 2, 267822980, 325286564, 66697091, 239352431,
94380295, 1, 126621669, 158555699, 32951026, 23,
108000151, 132505189, 29587564, 120381505, 25106680,
117506099, 22868767, 115940080, 22878163, 119286731,
22881061),
.Dim = c(23L, 1L),
index = structure(c(1490990400, 1490994000, 1490997600,
1491001200, 1491004800, 1491008400,
1491012000, 1491026400, 1491033600,
1491037200, 1491040800, 1491058800,
1491062400, 1491066000, 1491069600,
1491073200, 1491076800, 1491109200,
1491112800, 1491120000, 1491123600,
1491156000, 1491159600),
tzone = "US/Mountain",
tclass = c("POSIXct","POSIXt")),
class = c("xts", "zoo"),
.indexCLASS = c("POSIXct","POSIXt"),
tclass = c("POSIXct", "POSIXt"),
.indexTZ = "US/Mountain",
tzone = "US/Mountain",
.CLASS = "double",
.Dimnames = list(NULL, "count"))
require("forecast")
library(highcharter)
library(xts)
t.arima <- auto.arima(t)
x <- forecast(t.arima, level = c(95, 80))
# Problem
## Time from 'forecast'
time.x <- time(x$mean) # ts variable
time.x # see that frequency = 0.000277777777777778
## Original time
time.t <- time(t) # POSIXct variable, use as.ts to see frequency
as.ts(time.t) # frequency = 1
## Try to transform back to formatted date
as.POSIXct(as.double(time.t), tz = "US/Mountain", origin = "1970-01-01")
as.POSIXct(as.double(time.x), tz = "US/Mountain", origin = "1970-01-01")
#--------------------------------------------------------#
# SOLUTION 1
t.tmp <- ts(t, start=1, end = length(t))
t.arima.1 <- auto.arima(t.tmp)
x.1 <- forecast(t.arima.1, level = c(95, 80))
highchart(type = 'stock') %>%
hc_add_series(x.1) %>%
hc_add_series(x.1$x, name = "Original") %>%
hc_add_series(x.1$fitted, name = "Fitted")
#------------------------------------------------------#
# SOLUTION 2 - With correct dates but wrong plot
## Create new forecast variable
x.2 <- forecast(t.arima.1, level = c(95, 80))
## Take forecast length
forecast.length <- length(time.x)
### Create New Forecast dates (HOUR)
### Since I don't know the exact forecast times, I'll add one HOUR
### for each obs starting from the last date in the original dataset
last.date <- time.t[length(time.t)]
new.forecast.time.hour <- as.POSIXct(last.date) + c((1:forecast.length)*3600)
## Insert date back
x.2$mean <- xts(x.1$mean, order.by = new.forecast.time.hour)
x.2$lower <- xts(x.1$lower, order.by = new.forecast.time.hour)
x.2$upper <- xts(x.1$upper, order.by = new.forecast.time.hour)
### Original Data
x.2$x <- xts(x.1$x, order.by = time.t)
### Fitted
x.2$fitted <- xts(x.1$fitted, order.by = time.t)
# Plot forecasts with correct date
highchart(type = 'stock') %>%
hc_add_series(x.2) %>%
hc_add_series(x.2$x, name = "Original") %>%
hc_add_series(x.2$fitted, name = "Fitted") %>%
hc_xAxis(type = 'datetime')
#------------------------------------------------------#
# SOLUTION 3 - Correct plot but only for daily forecasts
## Create new forecast variable
x.3 <- forecast(t.arima.1, level = c(95, 80))
## Take forecast length
forecast.length <- length(time.x)
### Create New Forecast dates (DAY)
### Since I don't know the exact forecast times, I'll add one DAY
### for each obs starting from the last date in the original dataset
last.date <- time.t[length(time.t)]
new.forecast.time.day <- as.POSIXct(last.date) + c((1:forecast.length)*3600*24)
## Add change from as.POSIXct to as.Date
new.forecast.time.day <- as.Date(new.forecast.time.day)
## Insert date back
x.3$mean <- xts(x.1$mean, order.by = new.forecast.time.day)
x.3$lower <- xts(x.1$lower, order.by = new.forecast.time.day)
x.3$upper <- xts(x.1$upper, order.by = new.forecast.time.day)
### Original Data
x.3$x <- xts(x.1$x, order.by = time.t)
### Fitted
x.3$fitted <- xts(x.1$fitted, order.by = time.t)
# Plot forecasts with correct date
highchart(type = 'stock') %>%
hc_add_series(x.3) %>%
hc_add_series(x.3$x, name = "Original") %>%
hc_add_series(x.3$fitted, name = "Fitted") %>%
hc_xAxis(type = 'datetime')
另外一件事：我的图上的拟合值与 jbkunst 的图上的拟合值不同，因为他直接在 t 上使用了 forecast，而不是在 t.arima 上（我相信只是一个错字）。这样，我的预测是基于 Arima 模型，而他的预测是基于 ETS 模型。
这是
我正在尝试让该问题中给出的管道接受预测对象作为输入：
再次使用此数据：
> dput(t)
structure(c(2, 2, 267822980, 325286564, 66697091, 239352431,
94380295, 1, 126621669, 158555699, 32951026, 23, 108000151, 132505189,
29587564, 120381505, 25106680, 117506099, 22868767, 115940080,
22878163, 119286731, 22881061), .Dim = c(23L, 1L), index = structure(c(1490990400,
1490994000, 1490997600, 1491001200, 1491004800, 1491008400, 1491012000,
1491026400, 1491033600, 1491037200, 1491040800, 1491058800, 1491062400,
1491066000, 1491069600, 1491073200, 1491076800, 1491109200, 1491112800,
1491120000, 1491123600, 1491156000, 1491159600), tzone = "US/Mountain", tclass = c("POSIXct",
"POSIXt")), class = c("xts", "zoo"), .indexCLASS = c("POSIXct",
"POSIXt"), tclass = c("POSIXct", "POSIXt"), .indexTZ = "US/Mountain", tzone = "US/Mountain", .CLASS = "double", .Dimnames = list(
NULL, "count"))
我用
highchart(type = 'stock') %>%
hc_add_series(t) %>%
hc_xAxis(type = 'datetime')
创建
但是如果我按照同样的食谱使用
require("forecast")
t.arima <- auto.arima(t)
x <- forecast(t.arima, level = c(95, 80))
highchart(type = 'stock') %>%
hc_add_series(x) %>%
hc_xAxis(type = 'datetime')
我收到这个错误：
Error in as.Date.ts(.) : unable to convert ts time to Date class
如何将预测系列与历史系列一起显示？我在文档中看到过这个，但不明白为什么我会收到这个错误。
JK 的 JS 控制台输出：
dput(df)
structure(list(Index = structure(c(1490968800, 1490972400, 1490976000,
1490979600, 1490983200, 1490986800, 1490990400, 1491004800, 1491012000,
1491015600, 1491019200, 1491037200, 1491040800, 1491044400, 1491048000,
1491051600, 1491055200, 1491087600, 1491091200, 1491098400, 1491102000,
1491134400, 1491138000, 1491217200, 1491220800, 1491224400, 1491228000,
1491231600, 1491235200, 1491238800, 1491242400, 1491246000, 1491249600,
1491253200, 1491256800, 1491260400, 1491264000, 1491267600), class = c("POSIXct",
"POSIXt")), Data = c(2, 2, 259465771, 315866206, 64582553, 233440220,
91918347, 1, 126563786, 158555699, 32951026, 23, 108000151, 132505189,
29587564, 120381505, 25106680, 117506099, 22868767, 115898351,
22878163, 119285747, 22881061, 157925588, 32447780, 223096830,
281656273, 45406684, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA),
Fitted = c(102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA),
`Point Forecast` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143, 102170573.857143,
102170573.857143, 102170573.857143, 102170573.857143), `Lo 80` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -16003477.5789723,
-16003477.5789723, -16003477.5789723, -16003477.5789723,
-16003477.5789723, -16003477.5789723, -16003477.5789723,
-16003477.5789723, -16003477.5789723, -16003477.5789723),
`Hi 80` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 220344625.293258, 220344625.293258, 220344625.293258,
220344625.293258, 220344625.293258, 220344625.293258, 220344625.293258,
220344625.293258, 220344625.293258, 220344625.293258), `Lo 95` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -78561041.5917782,
-78561041.5917782, -78561041.5917782, -78561041.5917782,
-78561041.5917782, -78561041.5917782, -78561041.5917782,
-78561041.5917782, -78561041.5917782, -78561041.5917782),
`Hi 95` = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 282902189.306064, 282902189.306064, 282902189.306064,
282902189.306064, 282902189.306064, 282902189.306064, 282902189.306064,
282902189.306064, 282902189.306064, 282902189.306064)), .Names = c("Index",
"Data", "Fitted", "Point Forecast", "Lo 80", "Hi 80", "Lo 95",
"Hi 95"), row.names = c(NA, -38L), class = "data.frame")
不确定这是因为时间序列不规则。
总之，ggfortify:::fortify.forecast是你的朋友。为什么？因为 fortify（尝试）转换数据框中的所有 R 对象。所以：
library(highcharter)
library(forecast)
t.arima <- auto.arima(t)
x <- forecast(t, level = c(95, 80))
library(highcharter)
library(ggplot2)
library(ggfortify)
#>
#> Attaching package: 'ggfortify'
#> The following object is masked from 'package:forecast':
#>
#> gglagplot
class(x)
#> [1] "forecast"
df <- fortify(x)
head(df)
#> Index Data Fitted Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
#> 1 1 2 140658844 NA NA NA NA NA
#> 2 3601 2 121734145 NA NA NA NA NA
#> 3 7201 267822980 105355638 NA NA NA NA NA
#> 4 10801 325286564 127214522 NA NA NA NA NA
#> 5 14401 66697091 153863779 NA NA NA NA NA
#> 6 18001 239352431 142136089 NA NA NA NA NA
现在您可以：
highchart(type = "stock") %>%
hc_add_series(df, "line", hcaes(Index, Data), name = "Original") %>%
hc_add_series(df, "line", hcaes(Index, Fitted), name = "Fitted") %>%
hc_add_series(df, "line", hcaes(Index, `Point Forecast`), name = "Forecast") %>%
hc_add_series(df, "arearange", hcaes(Index, low = `Lo 80`, high = `Hi 80`), name = "Interval")
如您所见，fortify也检测不到实时。所以你需要在你想要的时候转换索引。
错误
Error in as.Date.ts(.) : unable to convert ts time to Date class
是因为你有一个频率未被函数覆盖的ts对象as.Date.ts(.)。当我们看到这个函数做了什么时，这就是我们得到的：
function (x, offset = 0, ...)
{
time.x <- unclass(time(x)) + offset
if (frequency(x) == 1)
as.Date(paste(time.x, 1, 1, sep = "-"))
else if (frequency(x) == 4)
as.Date(paste((time.x + 0.001)%/%1, 3 * (cycle(x) - 1) +
1, 1, sep = "-"))
else if (frequency(x) == 12)
as.Date(paste((time.x + 0.001)%/%1, cycle(x), 1, sep = "-"))
else stop("unable to convert ts time to Date class")
}
此函数仅考虑 ts 对象的频率的 3 个值：1、4 或 12。当我们查看您的对象的频率 x 时，我们发现它 frequency = 0.000277777777777778，所以当 highcharter 使用 x 中的 ts 对象调用函数时，它会停止并给你那个错误。
关于如何 "fix" 我们有两个选择：
- 用
frequency = 1before运行auto.arima将t转换为ts对象（而不是xts对象）和forecast; - 经过运行
auto.arima和forecast之后，我们可以为未来的日期创建一个索引，并转换[=中的ts对象15=] 到具有正确索引的 xts 对象中。
我说 "fix" 因为这些解决方案并不完美，我们将看到。
选项 1
t <- structure(
c(2, 2, 267822980, 325286564, 66697091, 239352431,
94380295, 1, 126621669, 158555699, 32951026, 23,
108000151, 132505189, 29587564, 120381505, 25106680,
117506099, 22868767, 115940080, 22878163, 119286731,
22881061),
.Dim = c(23L, 1L),
index = structure(c(1490990400, 1490994000, 1490997600,
1491001200, 1491004800, 1491008400,
1491012000, 1491026400, 1491033600,
1491037200, 1491040800, 1491058800,
1491062400, 1491066000, 1491069600,
1491073200, 1491076800, 1491109200,
1491112800, 1491120000, 1491123600,
1491156000, 1491159600),
tzone = "US/Mountain",
tclass = c("POSIXct","POSIXt")),
class = c("xts", "zoo"),
.indexCLASS = c("POSIXct","POSIXt"),
tclass = c("POSIXct", "POSIXt"),
.indexTZ = "US/Mountain",
tzone = "US/Mountain",
.CLASS = "double",
.Dimnames = list(NULL, "count"))
require("forecast")
library(highcharter)
# SOLUTION 1
t.tmp <- ts(t, start=1, end = length(t))
t.arima.1 <- auto.arima(t.tmp)
x.1 <- forecast(t.arima.1, level = c(95, 80))
highchart(type = 'stock') %>%
hc_add_series(x.1) %>%
hc_add_series(x.1$x, name = "Original") %>%
hc_add_series(x.1$fitted, name = "Fitted")
这种方法的问题是我们丢失了日期（轴、工具提示等）。
选项 2，第一次尝试：每小时预报
我试图为未来值创建一个小时索引，但出于某种原因，Highcharter 将间隔向左移动（或者日期存在一些问题，我无法 see/figure 出来）。
选项 2，第二次尝试：每日预报
当我将其更改为未来值的每日指数时，它起作用了，但这很奇怪，因为我们有每小时的观察结果，并且我们的图表的预测部分显示 "daily forecasts"。
完整代码如下：
t <- structure(
c(2, 2, 267822980, 325286564, 66697091, 239352431,
94380295, 1, 126621669, 158555699, 32951026, 23,
108000151, 132505189, 29587564, 120381505, 25106680,
117506099, 22868767, 115940080, 22878163, 119286731,
22881061),
.Dim = c(23L, 1L),
index = structure(c(1490990400, 1490994000, 1490997600,
1491001200, 1491004800, 1491008400,
1491012000, 1491026400, 1491033600,
1491037200, 1491040800, 1491058800,
1491062400, 1491066000, 1491069600,
1491073200, 1491076800, 1491109200,
1491112800, 1491120000, 1491123600,
1491156000, 1491159600),
tzone = "US/Mountain",
tclass = c("POSIXct","POSIXt")),
class = c("xts", "zoo"),
.indexCLASS = c("POSIXct","POSIXt"),
tclass = c("POSIXct", "POSIXt"),
.indexTZ = "US/Mountain",
tzone = "US/Mountain",
.CLASS = "double",
.Dimnames = list(NULL, "count"))
require("forecast")
library(highcharter)
library(xts)
t.arima <- auto.arima(t)
x <- forecast(t.arima, level = c(95, 80))
# Problem
## Time from 'forecast'
time.x <- time(x$mean) # ts variable
time.x # see that frequency = 0.000277777777777778
## Original time
time.t <- time(t) # POSIXct variable, use as.ts to see frequency
as.ts(time.t) # frequency = 1
## Try to transform back to formatted date
as.POSIXct(as.double(time.t), tz = "US/Mountain", origin = "1970-01-01")
as.POSIXct(as.double(time.x), tz = "US/Mountain", origin = "1970-01-01")
#--------------------------------------------------------#
# SOLUTION 1
t.tmp <- ts(t, start=1, end = length(t))
t.arima.1 <- auto.arima(t.tmp)
x.1 <- forecast(t.arima.1, level = c(95, 80))
highchart(type = 'stock') %>%
hc_add_series(x.1) %>%
hc_add_series(x.1$x, name = "Original") %>%
hc_add_series(x.1$fitted, name = "Fitted")
#------------------------------------------------------#
# SOLUTION 2 - With correct dates but wrong plot
## Create new forecast variable
x.2 <- forecast(t.arima.1, level = c(95, 80))
## Take forecast length
forecast.length <- length(time.x)
### Create New Forecast dates (HOUR)
### Since I don't know the exact forecast times, I'll add one HOUR
### for each obs starting from the last date in the original dataset
last.date <- time.t[length(time.t)]
new.forecast.time.hour <- as.POSIXct(last.date) + c((1:forecast.length)*3600)
## Insert date back
x.2$mean <- xts(x.1$mean, order.by = new.forecast.time.hour)
x.2$lower <- xts(x.1$lower, order.by = new.forecast.time.hour)
x.2$upper <- xts(x.1$upper, order.by = new.forecast.time.hour)
### Original Data
x.2$x <- xts(x.1$x, order.by = time.t)
### Fitted
x.2$fitted <- xts(x.1$fitted, order.by = time.t)
# Plot forecasts with correct date
highchart(type = 'stock') %>%
hc_add_series(x.2) %>%
hc_add_series(x.2$x, name = "Original") %>%
hc_add_series(x.2$fitted, name = "Fitted") %>%
hc_xAxis(type = 'datetime')
#------------------------------------------------------#
# SOLUTION 3 - Correct plot but only for daily forecasts
## Create new forecast variable
x.3 <- forecast(t.arima.1, level = c(95, 80))
## Take forecast length
forecast.length <- length(time.x)
### Create New Forecast dates (DAY)
### Since I don't know the exact forecast times, I'll add one DAY
### for each obs starting from the last date in the original dataset
last.date <- time.t[length(time.t)]
new.forecast.time.day <- as.POSIXct(last.date) + c((1:forecast.length)*3600*24)
## Add change from as.POSIXct to as.Date
new.forecast.time.day <- as.Date(new.forecast.time.day)
## Insert date back
x.3$mean <- xts(x.1$mean, order.by = new.forecast.time.day)
x.3$lower <- xts(x.1$lower, order.by = new.forecast.time.day)
x.3$upper <- xts(x.1$upper, order.by = new.forecast.time.day)
### Original Data
x.3$x <- xts(x.1$x, order.by = time.t)
### Fitted
x.3$fitted <- xts(x.1$fitted, order.by = time.t)
# Plot forecasts with correct date
highchart(type = 'stock') %>%
hc_add_series(x.3) %>%
hc_add_series(x.3$x, name = "Original") %>%
hc_add_series(x.3$fitted, name = "Fitted") %>%
hc_xAxis(type = 'datetime')
另外一件事：我的图上的拟合值与 jbkunst 的图上的拟合值不同，因为他直接在 t 上使用了 forecast，而不是在 t.arima 上（我相信只是一个错字）。这样，我的预测是基于 Arima 模型，而他的预测是基于 ETS 模型。