这个时间序列预测模型（在R中）能否得到进一步改善？

我正在尝试 Build 这种预测模型，但无法获得令人印象深刻的结果 . 低号没有 . 培训模型的记录是不太好的结果的原因之一，我相信，所以我正在寻求帮助 .

这是预测变量的时间序列矩阵 . 这里Paidts7变量实际上是Paidts6的滞后变量 . XREG =

Paidts2  Paidts6  Paidts7 Paidts4 Paidts5  Paidts8
Jan 2014 32932400 29703000 58010000   21833   38820 102000.0
Feb 2014 33332497 35953000 29703000   10284   38930 104550.0
Mar 2014 35811723 40128000 35953000   11132   39840 104550.0
Apr 2014 28387000 29167000 40128000   13171   40010 104550.0
May 2014 27941601 27942000 29167000    9192   39640 104550.0
Jun 2014 34236746 35010000 27942000    8766   39430 104550.0
Jul 2014 22986887 26891000 35010000   11217   39060 104550.0
Aug 2014 31616679 31990000 26891000    8118   38840 104550.0
Sep 2014 41839591 46052000 31990000   10954   38380 104550.0
Oct 2014 36945266 36495000 46052000   14336   37920 104550.0
Nov 2014 44026966 41716000 36495000   12362   36810 104550.0
Dec 2014 57689000 60437000 41716000   14498   36470 104550.0
Jan 2015 35150678 35263000 60437000   22336   34110 104550.0
Feb 2015 33477565 33749000 35263000   12188   29970 107163.8
Mar 2015 41226928 41412000 33749000   11122   28580 107163.8
Apr 2015 31031405 30588000 41412000   12605   28970 107163.8
May 2015 31091543 29327000 30588000    9520   27820 107163.8
Jun 2015 38212015 35818000 29327000   10445   28880 107163.8
Jul 2015 32523660 32102000 35818000   12006   28730 107163.8
Aug 2015 33749299 33482000 32102000    9303   27880 107163.8
Sep 2015 48275932 44432000 33482000   10624   25950 107163.8
Oct 2015 32067045 32542000 44432000   15324   25050 107163.8
Nov 2015 46361434 40862000 32542000   10706   25190 107163.8
Dec 2015 68206802 71005000 40862000   14499   24670 107163.8
Jan 2016 34847451 29226000 71005000   23578   23100 107163.8
Feb 2016 34249625 43835001 29226000   13520   21430 109842.9
Mar 2016 45707923 56087003 43835001   15247   19980 109842.9
Apr 2016 33512366 37116000 56087003   18797   20900 109842.9
May 2016 33844153 42902002 37116000   11870   21520 109842.9
Jun 2016 40251630 53203010 42902002   14374   23150 109842.9
Jul 2016 33947604 38411008 53203010   18436   24230 109842.9
Aug 2016 35391779 38545003 38411008   11654   24050 109842.9
Sep 2016 49399281 55589008 38545003   13448   23510 109842.9
Oct 2016 36463617 45751005 55589008   19871   23940 109842.9
Nov 2016 45182618 51641006 45751005   14998   24540 109842.9
Dec 2016 64894588 79141002 51641006   18143   24390 109842.9

这是Y变量（待预测）

Jan       Feb       Mar       Apr       May       Jun
2014 1266757.8 1076023.4 1285495.7 1026840.2  910148.8 1111744.5
2015 1654745.7 1281946.6 1372669.3 1017266.6  841578.4 1353995.5
2016 1062048.8 1860531.1 1684564.3 1261672.0 1249547.7 1829317.9
       Jul       Aug       Sep       Oct       Nov       Dec
2014  799973.1  870778.9 1224827.3 1179754.0 1186726.3 1673259.5
2015 1127006.2  779374.9 1223445.6  925473.6 1460704.8 1632066.2
2016 1410316.4 1276771.1 1668296.7 1477083.3 1466419.2 2265343.3

我尝试了使用外部回归器的Forecast :: ARIMA和Forecast :: NNETAR模型，但是没有将MAPE带到7以下 . 我的目标是MAPE低于3且RMSE低于50000.欢迎使用任何其他包和功能 .

这是测试数据：XREG =

Paidts2test Paidts6test Paidts7test Paidts4test
Jan 2017    31012640    36892000    79141002       27912
Feb 2017    33009746    39020000    36892000        9724
Mar 2017    39296653    52787000    39020000       11335
Apr 2017    36387649    36475000    52787000       17002
May 2017    40269571    41053000    36475000       11436
          Paidts5test Paidts8test
Jan 2017       25100    109842.9
Feb 2017       25800    112589.0
Mar 2017       25680    112589.0
Apr 2017       25540    112589.0
May 2017       25830    112589.0

Y = 1627598 1041766 1381536 1346429 1314992

如果您发现删除一个或多个预测变量会显着改善结果，请继续 . 我们将非常感谢您的帮助，请在“R”中建议不要在其他工具中使用 .

-谢谢