| 347 |
494.7259 |
| 296 |
423.1596 |
| 566 |
451.9351 |
| 820 |
789.1606 |
| 919 |
1029.0407 |
| 1379 |
1048.4620 |
| 1159 |
1137.0253 |
| 1132 |
1150.5906 |
| 1119 |
1191.6808 |
| 1238 |
1270.3948 |
| 904 |
715.5118 |
| 699 |
538.0244 |
| 347 |
467.0779 |
| 296 |
433.5630 |
| 566 |
488.5433 |
| 820 |
775.7648 |
| 919 |
1025.4609 |
| 1379 |
1059.2984 |
| 1159 |
1060.0561 |
| 1132 |
1157.8419 |
| 1119 |
1139.3621 |
| 1238 |
1210.0183 |
| 904 |
824.4479 |
| 699 |
526.6290 |
## # A tibble: 12 × 3
## Month.YrInventory BestForecast2015 SeasonIndexB
## <chr> <dbl> <dbl>
## 1 January 2015 494.7259 0.5016624
## 2 February 2015 423.1596 0.4685116
## 3 March 2015 451.9351 0.6090358
## 4 April 2015 789.1606 0.9522785
## 5 May 2015 1029.0407 1.1922550
## 6 June 2015 1048.4620 1.3178173
## 7 July 2015 1137.0253 1.3216311
## 8 August 2015 1150.5906 1.3392333
## 9 September 2015 1191.6808 1.3606493
## 10 October 2015 1270.3948 1.3976139
## 11 November 2015 715.5118 0.8874438
## 12 December 2015 538.0244 0.6518678
| January 2015 |
24 |
8 |
118 |
35 |
153 |
| February 2015 |
20 |
7 |
101 |
33 |
133 |
| March 2015 |
22 |
9 |
108 |
42 |
150 |
| April 2015 |
38 |
14 |
188 |
66 |
254 |
| May 2015 |
49 |
18 |
245 |
83 |
328 |
| June 2015 |
50 |
20 |
250 |
92 |
342 |
| July 2015 |
54 |
20 |
271 |
92 |
363 |
| August 2015 |
55 |
20 |
274 |
93 |
367 |
| September 2015 |
57 |
21 |
284 |
95 |
379 |
| October 2015 |
60 |
21 |
302 |
97 |
400 |
| November 2015 |
34 |
14 |
170 |
62 |
232 |
| December 2015 |
26 |
10 |
128 |
45 |
174 |
| January 2015 |
24 |
16 |
118 |
72 |
190 |
| February 2015 |
20 |
16 |
101 |
72 |
173 |
| March 2015 |
22 |
16 |
108 |
73 |
180 |
| April 2015 |
38 |
16 |
188 |
74 |
262 |
| May 2015 |
49 |
16 |
245 |
75 |
320 |
| June 2015 |
50 |
17 |
250 |
76 |
325 |
| July 2015 |
54 |
17 |
271 |
76 |
347 |
| August 2015 |
55 |
17 |
274 |
76 |
350 |
| September 2015 |
57 |
17 |
284 |
76 |
360 |
| October 2015 |
60 |
17 |
302 |
76 |
379 |
| November 2015 |
34 |
16 |
170 |
74 |
244 |
| December 2015 |
26 |
16 |
128 |
73 |
201 |
Naive
| Training set |
4.4 |
215.7496 |
160.4571 |
-3.520532 |
21.77095 |
1 |
0.288158 |
| Test set |
-130.5 |
132.1003 |
130.5000 |
-26.771912 |
26.77191 |
NA |
NA |
RWF
| Training set |
4.4 |
215.7496 |
160.4571 |
-3.520532 |
21.77095 |
1.000000 |
0.288158 |
| Test set |
299.9 |
409.2763 |
352.1000 |
24.439203 |
35.14797 |
2.194355 |
NA |
SMA
| Training set |
-14.27173 |
91.7064 |
79.29703 |
1.04432 |
11.23638 |
0.6433177 |
0.4521287 |
| Test set |
1432.14500 |
1701.1412 |
1439.80938 |
136.66123 |
138.15818 |
11.6808267 |
NA |
SES
| Training set |
4.278615 |
212.7381 |
156.0029 |
-3.423313 |
21.16665 |
0.9722402 |
0.2880565 |
| Test set |
299.904697 |
409.2798 |
352.1028 |
24.439774 |
35.14816 |
2.1943730 |
NA |
ETS
## ETS(A,N,N)
##
## Call:
## ets(y = Ctrain)
##
## Smoothing parameters:
## alpha = 0.9999
##
## Initial states:
## l = 468.6699
##
## sigma: 212.7382
##
## AIC AICc BIC
## 520.9312 521.6812 525.6817
## ME RMSE MAE MPE MAPE MASE
## Training set 4.259471 212.7382 156.0210 -3.427416 21.17052 0.9723531
## Test set 178.004774 341.0000 265.0016 11.946853 29.79414 NA
## ACF1
## Training set 0.2880284
## Test set NA
| Training set |
4.259471 |
212.7382 |
156.0210 |
-3.427416 |
21.17052 |
0.9723531 |
0.2880284 |
| Test set |
178.004774 |
341.0000 |
265.0016 |
11.946853 |
29.79414 |
NA |
NA |
Holt
| Training set |
-4.730953 |
214.5025 |
158.9148 |
-5.01515 |
21.91948 |
0.9903876 |
0.2979241 |
| Test set |
156.429150 |
321.3651 |
249.3810 |
9.38250 |
28.46142 |
NA |
NA |
Arima
## Series: B
## ARIMA(0,1,1)(0,1,1)[12]
##
## Coefficients:
## ma1 sma1
## -0.7438 -0.5104
## s.e. 0.1110 0.2052
##
## sigma^2 estimated as 19740: log likelihood=-287.58
## AIC=581.16 AICc=581.74 BIC=586.58
## ME RMSE MAE MPE MAPE MASE
## Training set 7.688023 120.9745 83.0515 -0.9960218 10.74112 0.478127
## Test set 131.208585 286.9497 229.8357 10.2290856 27.46751 1.323163
## ACF1
## Training set -0.01263556
## Test set NA
BestForecast
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Jan 2105 494.7259 318.0591 671.3926 224.5374 764.9143
## Feb 2105 423.1596 235.9723 610.3469 136.8814 709.4379
## Mar 2105 451.9351 254.7879 649.0823 150.4244 753.4457
## Apr 2105 789.1606 582.5330 995.7882 473.1509 1105.1703
## May 2105 1029.0407 813.3489 1244.7324 699.1687 1358.9127
## Jun 2105 1048.4620 824.0720 1272.8520 705.2871 1391.6368
## Jul 2105 1137.0253 904.2619 1369.7887 781.0444 1493.0062
## Aug 2105 1150.5906 909.7447 1391.4365 782.2486 1518.9326
## Sep 2105 1191.6808 943.0150 1440.3466 811.3793 1571.9823
## Oct 2105 1270.3948 1014.1476 1526.6420 878.4985 1662.2910
## Nov 2105 715.5118 451.9012 979.1225 312.3541 1118.6695
## Dec 2105 538.0244 267.2504 808.7983 123.9114 952.1373
ETS()2015 Forecast Second Best MAPE, Best AIC
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Jan 2105 467.0779 394.0232 540.1325 355.3504 578.8053
## Feb 2105 433.5630 363.2208 503.9052 325.9838 541.1422
## Mar 2105 488.5433 407.9845 569.1021 365.3392 611.7474
## Apr 2105 775.7648 650.2409 901.2887 583.7926 967.7370
## May 2105 1025.4609 858.9559 1191.9659 770.8135 1280.1083
## Jun 2105 1059.2984 882.8443 1235.7525 789.4351 1329.1617
## Jul 2105 1060.0561 878.4621 1241.6501 782.3320 1337.7802
## Aug 2105 1157.8419 957.6237 1358.0602 851.6346 1464.0493
## Sep 2105 1139.3621 936.3761 1342.3481 828.9218 1449.8025
## Oct 2105 1210.0183 992.4114 1427.6252 877.2172 1542.8194
## Nov 2105 824.4479 649.7491 999.1468 557.2691 1091.6268
## Dec 2105 526.6290 379.0541 674.2038 300.9327 752.3252
## Jan 2106 467.0779 322.8925 611.2632 246.5654 687.5903
## Feb 2106 433.5630 290.6792 576.4468 215.0411 652.0849
## Mar 2106 488.5433 340.3107 636.7759 261.8411 715.2454
## Apr 2106 775.7648 598.9757 952.5539 505.3892 1046.1404
## May 2106 1025.4609 817.5259 1233.3959 707.4518 1343.4700
## Jun 2106 1059.2984 843.2792 1275.3176 728.9256 1389.6712
## Jul 2106 1060.0561 839.7847 1280.3275 723.1801 1396.9321
## Aug 2106 1157.8419 921.9488 1393.7351 797.0745 1518.6094
## Sep 2106 1139.3621 901.0860 1377.6383 774.9502 1503.7741
## Oct 2106 1210.0183 959.1410 1460.8956 826.3345 1593.7021
## Nov 2106 824.4479 609.6893 1039.2066 496.0030 1152.8929
## Dec 2106 526.6290 333.2515 720.0065 230.8836 822.3743
## ETS(M,N,A)
##
## Call:
## ets(y = variableB, model = "MNA")
##
## Smoothing parameters:
## alpha = 0.254
## gamma = 1e-04
##
## Initial states:
## l = 890.5525
## s=-320.7139 -22.8562 362.6931 292.0145 310.5107 212.7001
## 211.968 178.1159 -71.586 -358.8038 -413.7806 -380.2618
##
## sigma: 0.122
##
## AIC AICc BIC
## 487.5074 511.5074 511.2602
## Point.Forecast Lo.80 Hi.80 Lo.95 Hi.95
## Jan 2105 467.0779 394.0232 540.1325 355.3504 578.8053
## Feb 2105 433.5630 363.2208 503.9052 325.9838 541.1422
## Mar 2105 488.5433 407.9845 569.1021 365.3392 611.7474
## Apr 2105 775.7648 650.2409 901.2887 583.7926 967.7370
## May 2105 1025.4609 858.9559 1191.9659 770.8135 1280.1083
## Jun 2105 1059.2984 882.8443 1235.7525 789.4351 1329.1617
## Jul 2105 1060.0561 878.4621 1241.6501 782.3320 1337.7802
## Aug 2105 1157.8419 957.6237 1358.0602 851.6346 1464.0493
## Sep 2105 1139.3621 936.3761 1342.3481 828.9218 1449.8025
## Oct 2105 1210.0183 992.4114 1427.6252 877.2172 1542.8194
## Nov 2105 824.4479 649.7491 999.1468 557.2691 1091.6268
## Dec 2105 526.6290 379.0541 674.2038 300.9327 752.3252
## Jan 2106 467.0779 322.8925 611.2632 246.5654 687.5903
## Feb 2106 433.5630 290.6792 576.4468 215.0411 652.0849
## Mar 2106 488.5433 340.3107 636.7759 261.8411 715.2454
## Apr 2106 775.7648 598.9757 952.5539 505.3892 1046.1404
## May 2106 1025.4609 817.5259 1233.3959 707.4518 1343.4700
## Jun 2106 1059.2984 843.2792 1275.3176 728.9256 1389.6712
## Jul 2106 1060.0561 839.7847 1280.3275 723.1801 1396.9321
## Aug 2106 1157.8419 921.9488 1393.7351 797.0745 1518.6094
## Sep 2106 1139.3621 901.0860 1377.6383 774.9502 1503.7741
## Oct 2106 1210.0183 959.1410 1460.8956 826.3345 1593.7021
## Nov 2106 824.4479 609.6893 1039.2066 496.0030 1152.8929
## Dec 2106 526.6290 333.2515 720.0065 230.8836 822.3743
SigmaB=sd(resultsB$Point.Forecast)/21 tableB <- data.frame(Month.Yr, B.Actual)
BForecast <- tail(resultsB, 13)
resultsB\(Month.Yr <- Month.Yr Product.B <- inner_join(tableB, resultsB, by = "Month.Yr") Product.B SigmaB #create a new column Product.B["Month_Yr"] <- NA # That creates the new column named "MY_NEW_COLUMN" filled with "NA" Product.B\)Month_Yr <- Product.B$Month.Yr