r - 预测将 forecast.gts 中的 xreg 参数与每个时间序列具有不同值的多个外部变量一起使用(hts 包)-6ren

r - 预测将 forecast.gts 中的 xreg 参数与每个时间序列具有不同值的多个外部变量一起使用(hts 包)

转载作者：行者123 更新时间：2023-12-02 17:35:01

我目前正在使用带有单个外部变量的 forecast.gts(hts 包)，该变量为所有单独的时间序列保存相同的值，以便创建 30 天的预测，但是我想要包括另一个外部回归器，它为每个单独的 ts 保存不同的值。

我将问题简化如下:

我有 4 个 ts 可以预测(它们是品牌和非大集团每天花费的保险付费搜索)，分为两组:

enter image description here

我有兴趣应用两个外部回归:

开放注册期(具有更高的值)- 这适用于所有四个系列
关闭期:这仅适用于两个非品牌 ts - 在 10/22/2013 - 12/21/2013 和 12/24/2013 - 01/27/2014 这两个时间段，它们被暂停，因此有 ~0 个值。

所以对于第一个外部回归量，我设法使用 forecast.hts 如下:

library(ggplot2)
library(plyr)
library(hts)
library(ts)
library(data.table)
library(reshape2)
horizon<-30
data1<-dcast(data, Date ~ Brand+Venue, sum , value.var="Spend")
data[is.na(data)] <- 0
abcd<-ts(as.matrix(data1[,2:5]),frequency=7)
#abcd<-abcd+0.000001
Brand <- c("Brand","Brand","NonBrand","NonBrand")
Venue <- rep(c("Google","MSN"),  2)
gcd <- rbind(Brand,Venue)
rownames(gcd) <- c("Brand","Venue")
x <- gts(abcd, groups = gcd)

dates = data.frame(Date=seq.Date(min(data$Date,na.rm =T), max(data$Date,na.rm =T), by = "day"))
dates$enrollment<-ifelse(dates$Date>=as.Date("2013-10-01") & dates$Date<=as.Date("2014-03-31"),1,
                         ifelse(dates$Date>=as.Date("2014-11-15") & dates$Date<=as.Date("2015-02-15"),1,0))

enrollment_prediction<-seq.Date(max(dates$Date)+1, max(dates$Date)+horizon, by = "day")
enrollment_prediction<-ifelse(enrollment_prediction>=as.Date("2013-10-01") & enrollment_prediction<=as.Date("2014-03-31"),1,
                              ifelse(enrollment_prediction>=as.Date("2014-11-15") & enrollment_prediction<=as.Date("2015-02-15"),1,0))

train<-cbind(dates[,2])
predict<-cbind(enrollment_prediction)

    cost_fcasts <- forecast(x, h = horizon, method = "comb",fmethod = "arima",xreg=train,newxreg=predict)

现在我的问题是:我如何创建另一个新的回归量(二元变量)，它将在 2013 年 10 月 22 日期间为所有品牌 ts 分配 1 值并为非品牌 ts 分配 0 值 - 12/21/2013 & 12/24/2013 - 01/27/2014?

数据的子集:

dput(data)
structure(list(Date = structure(c(16104, 16182, 16149, 16190, 
16156, 16146, 16108, 16154, 16132, 16149, 16110, 16105, 16120, 
16135, 16155, 16126, 16130, 16140, 16136, 16128, 16127, 16129, 
16113, 16137, 16178, 16118, 16142, 16107, 16106, 16131, 16126, 
16139, 16138, 16157, 16135, 16152, 16148, 16123, 16140, 16120, 
16144, 16161, 16155, 16122, 16139, 16129, 16106, 16130, 16106, 
16154, 16103, 16125, 16186, 16114, 16142, 16133, 16154, 16109, 
16111, 16183, 16180, 16146, 16141, 16151, 16121, 16167, 16116, 
16121, 16125, 16105, 16152, 16108, 16118, 16155, 16181, 16132, 
16153, 16134, 16165, 16132, 16174, 16134, 16160, 16135, 16171, 
16147, 16123, 16152, 16107, 16151, 16103, 16138, 16151, 16106, 
16166, 16117, 16107, 16144, 16117, 16145, 16137, 16130, 16157, 
16173, 16150, 16184, 16150, 16172, 16159, 16178, 16138, 16168, 
16184, 16153, 16159, 16181, 16133, 16129, 16122, 16154, 16176, 
16179, 16188, 16125, 16136, 16127, 16150, 16115, 16115, 16162, 
16143, 16111, 16131, 16109, 16136, 16119, 16160, 16131, 16156, 
16161, 16164, 16151, 16190, 16113, 16165, 16142, 16119, 16152, 
16167, 16133, 16139, 16121, 16178, 16133, 16124, 16114, 16108, 
16144, 16168, 16118, 16143, 16148, 16119, 16126, 16185, 16111, 
16136, 16116, 16158, 16107, 16176, 16189, 16142, 16177, 16169, 
16104, 16167, 16115, 16112, 16131, 16112, 16175, 16148, 16140, 
16119, 16172, 16125, 16179, 16172, 16122, 16185, 16143, 16173, 
16145, 16183, 16180, 16147, 16163, 16170, 16130, 16109, 16170, 
16179, 16143, 16104, 16150, 16153, 16157, 16155, 16144, 16120, 
16181, 16188, 16105, 16158, 16182, 16115, 16147, 16173, 16153, 
16169, 16116, 16113, 16171, 16180, 16127, 16146, 16118, 16177, 
16113, 16141, 16120, 16146, 16189, 16169, 16173, 16112, 16157, 
16149, 16135, 16121, 16147, 16148, 16111, 16149, 16140, 16134, 
16114, 16168, 16187, 16126, 16141, 16108, 16139, 16170, 16103, 
16128, 16105, 16112, 16145, 16163, 16109, 16175, 16187, 16122, 
16176, 16186, 16141, 16174, 16156, 16129, 16114, 16128, 16127, 
16132, 16171, 16169, 16162, 16170, 16103, 16160, 16116, 16104, 
16158, 16134, 16137, 16178, 16123, 16174, 16117, 16188, 16159, 
16128, 16123, 16145, 16172, 16165, 16166, 16180, 16124, 16174, 
16138, 16110, 16164, 16164, 16110, 16137, 16177, 16175, 16171, 
16176, 16175, 16177, 16181, 16156, 16110, 16179, 16163, 16168, 
16189, 16188, 16183, 16185, 16166, 16190, 16167, 16165, 16124, 
16166, 16182, 16164, 16117, 16185, 16187, 16162, 16186, 16190, 
16187, 16161, 16184, 16160, 16186, 16159, 16183, 16124, 16158, 
16161, 16162, 16182, 16163, 16189, 16184), class = "Date"), Spend = c(15842.58, 
4053.33, 12276.37, 3255.13, 34147.63, 10280.15, 1359.05, 10630.05, 
19970.06, 8264.08, 633.13, 2018.73, 4336.53, 27650.11, 26951.01, 
28489.2, 668.22, 33879.52, 21996.9, 19303.88, 23985.98, 1517.48, 
1766.36, 14539.82, 2356.67, 13442.66, 9853.06, 1460.34, 1885.7, 
9548.48, 6922.77, 29439.75, 14516.58, 15262.33, 3533.56, 1626.65, 
11114.59, 585.64, 9125.84, 17940.8, 3477.37, 342.48, 9852.61, 
2936.53, 8459.63, 15603.87, 1745.46, 3913.92, 14721.02, 4547.05, 
757.44, 5125.04, 1523.12, 15526.4, 45516.4, 24888.82, 3190.53, 
1065.25, 4356.01, 1142.47, 269.77, 38923.51, 39508.12, 690.53, 
17041.63, 1513.47, 604.92, 3320.61, 1821.44, 14143.89, 8629.41, 
8033.21, 1292.16, 2460.76, 706.36, 2804.23, 11942.11, 3084.68, 
65.93, 9886.54, 3934.09, 26347.99, 903.66, 7629.38, 2468.47, 
18017.85, 3720.39, 3023.53, 4375.57, 2851.58, 8886.96, 642.01, 
6702.67, 4350.53, 1262.83, 496.95, 13424.99, 802.84, 858.65, 
22996.32, 2929.39, 11457.34, 213.06, 1136.51, 9664.37, 1168.22, 
2212.77, 1170.14, 356.39, 848.81, 3164.11, 3458.27, 3612.13, 
2919.57, 7448.95, 1572.38, 8165.08, 1923.62, 15476.18, 27578.67, 
3455.62, 279.58, 0, 30040.45, 1530.41, 6577.7, 1725.36, 1396.46, 
1321.49, 3878.4, 7021.28, 8853.14, 1283.59, 1682.93, 5563.42, 
1697.88, 3841.95, 3508.54, 9989.89, 1374.71, 2466.79, 1450.81, 
1246.87, 17256.75, 1184.34, 4911.98, 4279.49, 842.95, 1509.02, 
1978.1, 4056.48, 2312.5, 2674.83, 2795.75, 1047.46, 1365.23, 
3367.7, 28421.9, 1208.81, 2975.87, 4314.07, 9691.88, 16614.88, 
1710.66, 2870, 2287.66, 3298, 3248.6, 443.4, 1647.22, 2901.93, 
3610.21, 1956.94, 2885.58, 3195.33, 2173.07, 3748.72, 2734.09, 
9179.23, 571.99, 3893.73, 3600.8, 2361.42, 4801.02, 2242.37, 
1249.61, 2885.95, 1097.31, 293.9, 2044.25, 1075.1, 1888.26, 413.3, 
2594.21, 3729.84, 881.72, 2179.57, 31.98, 637.2, 1133.57, 520.98, 
2940.35, 1942.11, 43361.33, 1837.33, 7429.81, 20603.36, 9059.43, 
2776.64, 3027.94, 1803.23, 4175.03, 4361.84, 4780.86, 5334.76, 
1266.33, 10430.92, 3988.57, 366.93, 3517.32, 294.78, 826.85, 
1920, 3048.21, 1966.12, 2332.73, 5522.68, 1855.98, 3034.97, 4473.6, 
9379.69, 2278.22, 2403.14, 1256.74, 1367.85, 1887.51, 1914.65, 
0, 3268.07, 1899.46, 1456.54, 9883.6, 2106.05, 2364.99, 1895.83, 
1836.96, 7785.42, 3784.51, 1379.38, 1542.7, 2937.07, 1620.28, 
1658.6, 2033.13, 3081.68, 1435.15, 2945.92, 1802.12, 2084.01, 
3324.74, 4623.88, 661.2, 4835.06, 342.56, 1398.99, 1131.91, 328.3, 
4903.98, 4993.82, 0, 6923.42, 1591.52, 1692.26, 1607.41, 1898.13, 
611.49, 1344.86, 1023.46, 1168.65, 532.7, 17004.37, 655.28, 5582.81, 
1116.49, 1811.79, 4365.59, 657.33, 1225.97, 1150.57, 40.46, 1543.24, 
1487.07, 7264.13, 528.31, 621, 333.81, 372.11, 446.69, 334.46, 
528.69, 1353.67, 1881.9, 1040.74, 969.57, 92.97, 57.16, 611.54, 
1104.2, 1478.91, 931.5, 582.02, 973.26, 753.14, 1365.5, 0, 1.61, 
318.24, 1116.22, 124.04, 0, 0, 0, 0, 0, 0, 0, 0, 3.75, 26.52, 
0, 0, 1.41, 0, 0, 0, 0, 0, 0, 0, 0, 18.57, 0, 2.7, 0, 24.74, 
0, 0, 0, 72.7, 0, 0, 0), Brand = c("NonBrand", "Brand", "NonBrand", 
"Brand", "NonBrand", "Brand", "NonBrand", "Brand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "NonBrand", "NonBrand", "NonBrand", 
"Brand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "Brand", 
"Brand", "NonBrand", "Brand", "NonBrand", "Brand", "NonBrand", 
"Brand", "NonBrand", "Brand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "Brand", "Brand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "NonBrand", "NonBrand", 
"Brand", "NonBrand", "NonBrand", "NonBrand", "Brand", "Brand", 
"NonBrand", "NonBrand", "NonBrand", "Brand", "NonBrand", "Brand", 
"Brand", "Brand", "NonBrand", "NonBrand", "Brand", "NonBrand", 
"NonBrand", "Brand", "Brand", "Brand", "NonBrand", "NonBrand", 
"NonBrand", "Brand", "Brand", "NonBrand", "NonBrand", "Brand", 
"NonBrand", "NonBrand", "Brand", "Brand", "NonBrand", "NonBrand", 
"Brand", "Brand", "NonBrand", "NonBrand", "Brand", "Brand", "Brand", 
"NonBrand", "Brand", "NonBrand", "Brand", "Brand", "Brand", "NonBrand", 
"Brand", "NonBrand", "NonBrand", "Brand", "NonBrand", "Brand", 
"Brand", "NonBrand", "Brand", "NonBrand", "Brand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "NonBrand", 
"Brand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "Brand", "Brand", "Brand", "NonBrand", 
"Brand", "Brand", "Brand", "NonBrand", "NonBrand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "Brand", 
"NonBrand", "Brand", "NonBrand", "Brand", "NonBrand", "Brand", 
"Brand", "Brand", "Brand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "Brand", "NonBrand", "NonBrand", 
"Brand", "NonBrand", "Brand", "NonBrand", "Brand", "Brand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "Brand", "Brand", "Brand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "NonBrand", 
"Brand", "Brand", "Brand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "Brand", "NonBrand", "NonBrand", "Brand", 
"Brand", "NonBrand", "NonBrand", "Brand", "Brand", "Brand", "NonBrand", 
"NonBrand", "NonBrand", "Brand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "Brand", "NonBrand", "Brand", "NonBrand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "NonBrand", 
"NonBrand", "Brand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"Brand", "Brand", "NonBrand", "Brand", "Brand", "NonBrand", "NonBrand", 
"Brand", "NonBrand", "NonBrand", "Brand", "Brand", "Brand", "Brand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "Brand", 
"NonBrand", "Brand", "Brand", "Brand", "Brand", "Brand", "NonBrand", 
"NonBrand", "NonBrand", "Brand", "Brand", "Brand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "NonBrand", "NonBrand", "Brand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "NonBrand", "Brand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "Brand", 
"Brand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"Brand", "Brand", "Brand", "Brand", "Brand", "Brand", "Brand", 
"Brand", "NonBrand", "Brand", "Brand", "NonBrand", "NonBrand", 
"Brand", "NonBrand", "NonBrand", "Brand", "Brand", "Brand", "Brand", 
"NonBrand", "NonBrand", "NonBrand", "Brand", "NonBrand", "Brand", 
"Brand", "Brand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "Brand", 
"NonBrand", "NonBrand", "NonBrand", "Brand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", 
"NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand", "NonBrand"
), Venue = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 
2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 
2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Google", "MSN"), class = "factor")), .Names = c("Date", 
"Spend", "Brand", "Venue"), row.names = c(16L, 24L, 31L, 54L, 
69L, 72L, 85L, 100L, 107L, 115L, 118L, 134L, 137L, 156L, 184L, 
195L, 198L, 226L, 284L, 289L, 290L, 321L, 345L, 358L, 359L, 361L, 
389L, 397L, 418L, 456L, 458L, 478L, 486L, 524L, 550L, 560L, 576L, 
588L, 598L, 600L, 625L, 649L, 735L, 775L, 782L, 791L, 891L, 896L, 
901L, 907L, 973L, 987L, 1209L, 1266L, 1276L, 1282L, 1304L, 1326L, 
1329L, 1350L, 1370L, 1537L, 1773L, 1775L, 1825L, 1921L, 2117L, 
2476L, 2478L, 2555L, 2556L, 2726L, 2761L, 2949L, 2974L, 3339L, 
3350L, 3438L, 3731L, 3843L, 3887L, 3890L, 3920L, 3922L, 3939L, 
3944L, 4021L, 4049L, 4072L, 4105L, 4138L, 4149L, 4174L, 4180L, 
4232L, 4261L, 4274L, 4281L, 4290L, 4316L, 4335L, 4417L, 4439L, 
4514L, 4565L, 4567L, 4632L, 4778L, 4787L, 4849L, 4889L, 4996L, 
5126L, 5199L, 5328L, 5342L, 5455L, 5466L, 5807L, 5932L, 6159L, 
6242L, 6611L, 6926L, 6928L, 6934L, 6963L, 6969L, 7018L, 7036L, 
7037L, 7039L, 7043L, 7121L, 7183L, 7186L, 7230L, 7237L, 7298L, 
7333L, 7358L, 7394L, 7505L, 7515L, 7520L, 7615L, 7648L, 7655L, 
7920L, 7964L, 8049L, 8079L, 8093L, 8364L, 8586L, 8596L, 8885L, 
8896L, 8906L, 9299L, 9506L, 9561L, 9625L, 9647L, 9688L, 9700L, 
9710L, 9722L, 9732L, 9754L, 9794L, 9798L, 9803L, 9868L, 9927L, 
9947L, 9951L, 9955L, 9984L, 9987L, 9998L, 10146L, 10169L, 10213L, 
10396L, 10409L, 10484L, 10505L, 10658L, 10678L, 10702L, 10710L, 
10841L, 10846L, 10932L, 10952L, 11037L, 11048L, 11213L, 11258L, 
11276L, 11282L, 11382L, 11432L, 11447L, 11777L, 11936L, 12024L, 
12027L, 12079L, 12092L, 12132L, 12173L, 12180L, 12246L, 12446L, 
12468L, 12501L, 12704L, 12736L, 12741L, 12780L, 12810L, 12816L, 
12852L, 13200L, 13539L, 13659L, 13879L, 13969L, 13998L, 14022L, 
14166L, 14537L, 14579L, 14708L, 15241L, 15712L, 15780L, 15784L, 
15814L, 15829L, 15862L, 15880L, 15915L, 15941L, 15977L, 15999L, 
16166L, 16494L, 16594L, 17368L, 17371L, 17376L, 17387L, 17401L, 
17433L, 17447L, 17462L, 17463L, 17476L, 17482L, 17630L, 17797L, 
17878L, 18051L, 18151L, 18461L, 18466L, 18488L, 18765L, 18766L, 
18780L, 18830L, 18831L, 18884L, 18925L, 19006L, 19022L, 19023L, 
19396L, 19510L, 19975L, 20000L, 20001L, 20043L, 20180L, 20587L, 
20685L, 20849L, 20885L, 21174L, 21182L, 21325L, 21411L, 21414L, 
21443L, 21735L, 21870L, 21969L, 22059L, 22279L, 22405L, 22620L, 
22648L, 22892L, 23073L, 23143L, 23992L, 24744L, 25054L, 25460L, 
25581L, 26298L, 26656L, 26684L, 26693L, 26858L, 28041L, 29034L, 
29345L, 29468L, 29634L, 29657L, 29675L, 29926L, 29967L, 30091L, 
30162L, 30203L, 30537L, 30569L, 30573L, 30762L, 30777L, 31110L, 
31247L, 31328L, 31684L, 32036L, 32151L, 32192L, 32375L, 32540L, 
32759L, 32896L, 33038L, 33643L, 33659L, 34136L, 34471L, 34652L
), class = "data.frame")

最佳答案

我不建议在您的示例中使用 forecast.gts()，因为针对不同时间序列处理不同的 xreg 仍在开发中。但还有另一种方法可以让您到达那里。

与其直接使用 forecast.gts()，您需要先使用 forecast::auto.arima 生成预测，然后应用 combinef() 到这些预测以获得协调的预测。

# Create a binary variable called "shutdown"
shutdown <- ifelse(dates$Date >= as.Date("2013-10-22") &
                   dates$Date <= as.Date("2013-12-21"), 0,
                   ifelse(dates$Date >= as.Date("2013-12-24") &
                   dates$Date <= as.Date("2014-01-27"), 0, 1))
pred.shutdown <- rep(1, horizon) # Assume it's open in the future
brand.xregs <- data.frame(enrollment = train)
pbrand.xregs <- data.frame(enrollment = predict)
nonbrand.xregs <- data.frame(enrollment = train, shutdown = shutdown)
pnonbrand.xregs <- data.frame(enrollment = predict, shutdown = pred.shutdown)
basef <- matrix(, nrow = horizon, ncol = 4) # bottom level
for (i in 1:2) { # brand
  basef[, i] <- forecast(auto.arima(abcd[, i], xreg = brand.xregs),
                     h = horizon, xreg = pbrand.xregs)$mean
}
for (i in 3:4) { # non brand
  basef[, i] <- forecast(auto.arima(abcd[, i], xreg = nonbrand.xregs), 
                     h = horizon, xreg = pnonbrand.xregs)$mean
 }
aggx <- aggts(x, levels = 0:2)
aggbasef <- matrix(, nrow = horizon, ncol = ncol(aggx)) # aggregated level
for (i in 1:ncol(aggx)) { # brand
  aggbasef[, i] <- forecast(auto.arima(aggx[, i]), h = horizon)$mean
}
allbasef <- cbind(aggbasef, basef) # All base forecasts across levels
rf <- combinef(allbasef, groups = x$groups)

希望这会有所帮助。

关于r - 预测将 forecast.gts 中的 xreg 参数与每个时间序列具有不同值的多个外部变量一起使用(hts 包)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/27734095/

文章推荐： list - Haskell获取IO列表元素

文章推荐： cuda - WebGL 或 CUDA 代码实际上是如何翻译成 GPU 指令的？

文章推荐： bash - 使用 dbus-send 的 DBus 自省(introspection)对象树

java - 如何解析名称=值^^名称=值^^名称=值
我的问题:非常具体。我正在尝试想出解析以下文本的最简单方法: ^^domain=domain_value^^version=version_value^^account_type=account_ty
Python:尝试创建与(或(值，值，值))的等价物
好吧，这就是我的困境: 我正在为 Reddit 子版 block 开发常见问题解答机器人。我在 bool 逻辑方面遇到了麻烦，需要一双更有经验的眼睛(这是我在 Python 中的第一次冒险)。现在，该
excel - 如何使循环同时运行所有 X 和 Y 值，现在它运行所有 Y 值，然后运行所有 X 值
它首先遍历所有 y 值，然后遍历所有 x 值。我需要 X 和 y 同时改变。 For x = 3 To lr + 1 For y = 2 To lr anyl.Cells(x, 1)
excel - SUMIF(条件，值)、SUMPROD(条件，值)和 SUM(条件*值)之间的区别
假设我有一个包含 2 列的 Excel 表格:单元格 A1 到 A10 中的日期和 B1 到 B10 中的值。我想对五月日期的所有值求和。我有3种可能性: {=SUM((MONTH(A1:A10)=
python - 将 Z 值(Z 值，标准分数)转换为 Python 中正态分布的 p 值
如何转换 Z-score来自 Z-distribution (standard normal distribution, Gaussian distribution)到 p-value ?我还没有找到
javascript( bool 值 ^ bool 值)
我正在重写一些 Javascript 代码以在 Excel VBA 中工作。由于在这个网站上搜索，我已经设法翻译了几乎所有的 Javascript 代码!但是，有些代码我无法准确理解它在做什么。这是一
php - 在php中的数组内添加新的键，值(值= 1)
我遇到过包含日期格式的时间戳日期的情况。然后我想构建一个图表，显示“点击”项目的数量“每天”， //array declaration $array1 = array("Date" => 0); $a
scala - Option(值)和Some(值)之间的差异
我是scala的新手! 我的问题是，是否有包含成员的案例类 myItem:Option[String] 当我构造类时，我需要将字符串内容包装在: Option("some string") 要么 So
php - 从表中选择 * 其中列 = 值 ^ column2 = 值
我正在用 PHP 创建一个登录系统。我需要用户使用他或她的用户名或电子邮件或电话号码登录然后使用密码。因为我知道在 Java 中我们会像 email==user^ username == user 这
c++ - WHERE 列 = 值，仅适用于 INTEGER 值
我在 C++ 项目上使用 sqlite，但是当我在具有文本值的列上使用 WHERE 时出现问题我创建了一个 sqlite 数据库: CREATE TABLE User( id INTEGER
c++ - 类类名(值)；和类类名=值；显式构造函数时的区别
当构造函数是显式时，它不用于隐式转换。在给定的代码片段中，构造函数被标记为 explicit。那为什么在 foo obj1(10.25); 情况下它可以工作，而在 foo obj2=10.25; 情况
c# - : if(! 值) 或 if(flag == 值) 哪个更清晰？
我知道这是一个主观问题，所以如果需要关闭它，我深表歉意，但我觉得它经常出现，让我想知道是否普遍偏爱一种形式而不是另一种形式。显然，最好的答案是“重构代码，这样你就不需要测试是否存在错误”，但有时没有
jquery - [属性~=值] 和 [属性*=值] 有什么区别？
这两个 jQuery 选择器有什么区别？以下是来自 w3schools.com 的定义: [attribute~=value] 选择器选择带有特定属性，其值包含特定字符串。 [attribute*=
CSS [属性|=值] 与 [属性*=值] 选择器
为什么我们需要CSS [attribute|=value] Selector根本当 CSS3 [attribute*=value] Selector基本上完成相同的事情，浏览器兼容性几乎相似？是否存在
java - 正则表达式仅验证 0 值。要验证的单个 0 值
我正在解决 regx 问题。我已经有一个像这样的 regx [0-9]*([.][0-9]{2})。这是 amont 格式验证。现在，通过此验证，我想包括不应提供 0 金额。比如 10 是有效的，但
java - 尽管代码删除了 "0"值，但为什么列表中仍保留 "0"值？
我正在研究计算机科学 A 考试的样题，但无法弄清楚为什么以下问题的正确答案是正确的。考虑以下方法。 public static void mystery(List nums) { for (
perl - 基本的 Perl 散列排序键，值，但也键 AND 值
好的，我正在编写一个 Perl 程序，它有一个我收集的值的哈希值(完全在一个完全独立的程序中)并提供给这个 Perl 脚本。这个散列是 (string,string) 的散列。我想通过 3 种方式对
mysql - 根据其他两列选择一列的 MAX 值，其中一列具有 MAX 值
我有一个表数据如下，来自不同的表。仅当第三列具有值“债务”并且第一列(日期)具有最大值时，我才想从第四列中获取最大值。最终值基于 MAX(DATE) 而不是 MAX(PRICE)。所以用简单的语言来说
php - 代码仅更新数据库中的 false 值，但不更新 true 值
我有一个奇怪的情况，只有错误状态保存到数据库中。当“状态”应该为 true 时，我的查询仍然执行 false。我有具有此功能的 Controller public function change_a
mysql SUM(值) 其中 SUM(值) > 2500
我有一个交易表(针对所需列进行了简化): id client_id value 1 1 200 2 2 150 3 1

行者123

个人简介

我是一名优秀的程序员,十分优秀！

作者热门文章

滴滴打车优惠券免费领取

全站热门文章

首页

博学

6Ren·AI

商城

r - 预测将 forecast.gts 中的 xreg 参数与每个时间序列具有不同值的多个外部变量一起使用(hts 包)