具有预测数据的数据处理

Question

[我正在尝试对两家商店（商店1和商店2）的销售进行预测。就像使用预测包进行预测的结果一样，我得到了这两个表。第一个表包含有关每个模型的MAPE误差数据（列值）。您可以看到数据和数据的屏幕截图。

Table_1<-structure(list(...1 = c("1", "2", "3", "4", "5", "6", "7", "8", 
                        "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", 
                        "20"), X1 = c("SNAIVE", "HW", "ETS", "ARIMA", "STL", "TBATS", 
                                      "NNETAR", "RWF", "TSLM", "FOURIER", "SNAIVE", "HW", "ETS", "ARIMA", 
                                      "STL", "TBATS", "NNETAR", "RWF", "TSLM", "FOURIER"), X2 = c("Store 1", 
                                                                                                  "Store 1", "Store 1", "Store 1", "Store 1", "Store 1", "Store 1", 
                                                                                                  "Store 1", "Store 1", "Store 1", "Store 2", "Store 2", "Store 2", 
                                                                                                  "Store 2", "Store 2", "Store 2", "Store 2", "Store 2", "Store 2", 
                                                                                                  "Store 2"), value = c(11.2819379803024, 4.90469397146697, 4.90469397146697, 
                                                                                                                        4.64808116952175, 4.92695563666538, 6.11286061911487, 7.66061575087076, 
                                                                                                                        8.95984865369006, 5.07614708345642, 4.57448859126253, 22.7760224588221, 
                                                                                                                        24.0502857269679, 18.9376978459644, 21.6693712888351, 21.6029490199174, 
                                                                                                                        24.692214948761, 26.2680955559159, 30.5302345480261, 22.2367412218357, 
                                                                                                                        22.6100823447494)), row.names = c(NA, -20L), class = c("tbl_df", 
                                                                                                                                                                               "tbl", "data.frame"))

使用黄色，我已经突出显示了商店1和商店2的预测中最好的三个模型（MAPE误差最低）。商店1是（ETS，ARIMA和Fourier），商店2（ETS，ARIMA和STL）。 >

第二张表包含所有这些模型每个月的预测数据。下面您可以看到数据以及数据的屏幕截图。

Table2<-structure(list(Date = structure(c(1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800, 1575158400, 1577836800, 1580515200, 1583020800, 1585699200, 1588291200, 1590969600, 1593561600, 1596240000, 1598918400, 1601510400, 1604188800, 1606780800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), Forecasting_model = c("SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "SNAIVE", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "HW", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ETS", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "ARIMA", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "STL", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "TBATS", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "NNAR", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "RWF", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "TSLM", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER", "FOURIER"), `Store 1` = c(8083, 1171, 1328, 1281, 1281, 1118, 1107, 1611, 1116, 1133, 1618, 1261, 8083, 8312, 1336, 1261, 1673, 1667, 1223, 1603, 1621, 1211, 1633, 1637, 1672, 8138, 8312, 1336, 1261, 1673, 1667, 1223, 1603, 1621, 1211, 1633, 1637, 1672, 8138, 8818, 1363, 1282, 1671, 1623, 1276, 1283, 1687, 1261, 1632, 1676, 1631, 8367, 8827, 1108, 1226, 1681, 1661, 1288, 1616, 1683, 1278, 1663, 1678, 1703, 8338, 8371, 1183, 1237, 1738, 1701, 1637, 1681, 1721, 1271, 1738, 1663, 1732, 8180, 8076, 1318, 1271, 1732, 1883, 1286, 1607, 1336, 1281, 1711, 1873, 1881, 8183, 1271, 1283, 1233, 1608, 1618, 1681, 1631, 1611, 1620, 1660, 1663, 1673, 1688, 8166, 1317, 1188, 1233, 1273, 1183, 1212, 1276, 1178, 1221, 1226, 1283, 8863, 8811, 1118, 1223, 1661, 1621, 1260, 1286, 1617, 1213, 1688, 1687, 1660, 8311), `Store 2` = c(1180, 811, 312, 1612, 1387, 878, 812, 883, 362, 768, 800, 760, 1180, 1021, 761, 1002, 1106, 1271, 337, 1113, 373, 833, 1012, 333, 303, 1166, 336, 708, 332, 1312, 1168, 838, 1010, 862, 773, 883, 861, 767, 1000, 1070, 636, 838, 1161, 1183, 887, 1001, 813, 331, 820, 738, 732, 1087, 333, 688, 810, 1311, 1183, 876, 338, 818, 816, 818, 816, 773, 333, 337, 888, 871, 1378, 1100, 1008, 368, 380, 883, 386, 872, 838, 363, 1102, 301, 831, 1133, 1331, 831, 333, 321, 338, 883, 832, 881, 1303, 766, 778, 773, 782, 731, 737, 801, 810, 816, 888, 883, 832, 811, 1820, 1000, 1136, 1270, 1718, 1188, 1873, 1162, 1136, 1130, 1178, 1110, 1371, 380, 703, 306, 1862, 1110, 873, 327, 837, 808, 817, 838, 726, 371)), row.names = c(NA, -130L), class = c("tbl_df", "tbl", "data.frame"))

因此，我的意图是根据最低的MAPE误差自动选择最佳的三个模型，例如上面突出显示的模型，并按以下示例为商店1和商店2的最佳三个模型按月平均计算。

我尝试过使用此代码，但是我不知道如何继续。

# Arrange data by MAPE error
Table_1a<-data.frame(Table_1)%>%
  select(X1,X2,value)%>%
  arrange((value),.by_group = TRUE)

# Select three best models
Table_1b <-data.frame(rbind(Table_1a[1:3, 1:3],Table_1a[10:13, 1:3]))%>%
  select(X1,X2)%>%
  group_by(X1,X2)

# Тhis line does not work 

Forecasting_Store_1<-mutate(Table_2,
                            ifelse(Table_1b$X1==Table_2$Forecasting_model,Table_2$Forecasting_model,"")
                         )
那么有人可以帮我解决这个问题吗？

我正在尝试对两家商店（商店1和商店2）的销售进行预测。就像通过预测包进行预测的结果一样，我得到了这两个表。第一个表包含有关MAPE错误的数据...

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