我正在尝试建立这个预测模型，但无法获得令人印象深刻的结果。低号。我相信，训练模型的记录是效果不佳的原因之一，因此我正在寻求帮助。
这是预测变量的时间序列矩阵。这里的 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
我尝试了带有外部回归器的 Forecast::ARIMA 和 Forecast::NNETAR 模型，但无法使 MAPE 低于 7。我的目标是 MAPE 低于 3，RMSE 低于 50000。欢迎您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可以显着改善结果，请继续。您的帮助将不胜感激，请仅在“R”中提出建议，而不是在其他工具中提出建议。
-谢谢