In [1]:
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import pyaf.Bench.NN3 as tNN3
%matplotlib inline
In [6]:
tester1 = tNN3.cNN_Tester(tsds.load_NN3_part1() , "NN3_PART_1");
#tester1.testAllSignals()
tester1.testSignals('NN3-001')
BENCH_TYPE NN3_PART_1 OneDataFrameForAllSignals
BENCH_DATA NN3_PART_1 <pyaf.Bench.TS_datasets.cTimeSeriesDatasetSpec object at 0x7ff38168e630>
TIME : Date N= 67 H= 2 HEAD= [0 1 2 3 4] TAIL= [62 63 64 65 66]
SIGNAL : NN3-001 N= 67 H= 2 HEAD= [ 5520. 3940. 4490. 5030. 5660.] TAIL= [ 6190. 5250. 5910. 6430. 5950.]
NN3-001 Date
0 5520.0 0
1 3940.0 1
2 4490.0 2
3 5030.0 3
4 5660.0 4
<class 'pandas.core.frame.DataFrame'>
Int64Index: 67 entries, 0 to 66
Data columns (total 2 columns):
NN3-001 67 non-null float64
Date 67 non-null int64
dtypes: float64(1), int64(1)
memory usage: 1.6 KB
None
Transformation Model \
0 _NN3-001 _NN3-001_ConstantTrend_residue_zeroCycle_resid...
1 _NN3-001 _NN3-001_ConstantTrend_residue_bestCycle_byL2_...
2 _NN3-001 _NN3-001_ConstantTrend_residue_bestCycle_byL2_...
3 _NN3-001 _NN3-001_ConstantTrend_residue_zeroCycle_resid...
4 CumSum_NN3-001 CumSum_NN3-001_LinearTrend_residue_zeroCycle_r...
5 _NN3-001 _NN3-001_Lag1Trend_residue_bestCycle_byL2_resi...
6 _NN3-001 _NN3-001_Lag1Trend_residue_zeroCycle_residue_NoAR
7 CumSum_NN3-001 CumSum_NN3-001_Lag1Trend_residue_zeroCycle_res...
8 Diff_NN3-001 Diff_NN3-001_Lag1Trend_residue_zeroCycle_resid...
9 RelDiff_NN3-001 RelDiff_NN3-001_Lag1Trend_residue_zeroCycle_re...
10 Diff_NN3-001 Diff_NN3-001_ConstantTrend_residue_bestCycle_b...
11 CumSum_NN3-001 CumSum_NN3-001_PolyTrend_residue_zeroCycle_res...
12 CumSum_NN3-001 CumSum_NN3-001_Lag1Trend_residue_zeroCycle_res...
13 CumSum_NN3-001 CumSum_NN3-001_ConstantTrend_residue_zeroCycle...
14 _NN3-001 _NN3-001_Lag1Trend_residue_zeroCycle_residue_A...
15 _NN3-001 _NN3-001_Lag1Trend_residue_bestCycle_byL2_resi...
16 CumSum_NN3-001 CumSum_NN3-001_Lag1Trend_residue_bestCycle_byL...
17 CumSum_NN3-001 CumSum_NN3-001_PolyTrend_residue_bestCycle_byL...
18 CumSum_NN3-001 CumSum_NN3-001_LinearTrend_residue_bestCycle_b...
19 _NN3-001 _NN3-001_PolyTrend_residue_bestCycle_byL2_resi...
20 Diff_NN3-001 Diff_NN3-001_LinearTrend_residue_bestCycle_byL...
21 _NN3-001 _NN3-001_LinearTrend_residue_bestCycle_byL2_re...
22 _NN3-001 _NN3-001_PolyTrend_residue_bestCycle_byL2_resi...
23 _NN3-001 _NN3-001_PolyTrend_residue_zeroCycle_residue_NoAR
24 _NN3-001 _NN3-001_LinearTrend_residue_bestCycle_byL2_re...
25 Diff_NN3-001 Diff_NN3-001_ConstantTrend_residue_zeroCycle_r...
26 _NN3-001 _NN3-001_LinearTrend_residue_zeroCycle_residue...
27 CumSum_NN3-001 CumSum_NN3-001_ConstantTrend_residue_bestCycle...
28 _NN3-001 _NN3-001_PolyTrend_residue_zeroCycle_residue_A...
29 _NN3-001 _NN3-001_LinearTrend_residue_zeroCycle_residue...
.. ... ...
34 Diff_NN3-001 Diff_NN3-001_Lag1Trend_residue_bestCycle_byL2_...
35 Diff_NN3-001 Diff_NN3-001_LinearTrend_residue_zeroCycle_res...
36 RelDiff_NN3-001 RelDiff_NN3-001_PolyTrend_residue_zeroCycle_re...
37 RelDiff_NN3-001 RelDiff_NN3-001_Lag1Trend_residue_zeroCycle_re...
38 RelDiff_NN3-001 RelDiff_NN3-001_PolyTrend_residue_bestCycle_by...
39 CumSum_NN3-001 CumSum_NN3-001_LinearTrend_residue_bestCycle_b...
40 RelDiff_NN3-001 RelDiff_NN3-001_Lag1Trend_residue_bestCycle_by...
41 Diff_NN3-001 Diff_NN3-001_ConstantTrend_residue_bestCycle_b...
42 Diff_NN3-001 Diff_NN3-001_ConstantTrend_residue_zeroCycle_r...
43 RelDiff_NN3-001 RelDiff_NN3-001_PolyTrend_residue_zeroCycle_re...
44 RelDiff_NN3-001 RelDiff_NN3-001_PolyTrend_residue_bestCycle_by...
45 Diff_NN3-001 Diff_NN3-001_PolyTrend_residue_bestCycle_byL2_...
46 RelDiff_NN3-001 RelDiff_NN3-001_ConstantTrend_residue_zeroCycl...
47 RelDiff_NN3-001 RelDiff_NN3-001_ConstantTrend_residue_bestCycl...
48 RelDiff_NN3-001 RelDiff_NN3-001_ConstantTrend_residue_zeroCycl...
49 RelDiff_NN3-001 RelDiff_NN3-001_LinearTrend_residue_zeroCycle_...
50 RelDiff_NN3-001 RelDiff_NN3-001_ConstantTrend_residue_bestCycl...
51 RelDiff_NN3-001 RelDiff_NN3-001_LinearTrend_residue_bestCycle_...
52 RelDiff_NN3-001 RelDiff_NN3-001_LinearTrend_residue_zeroCycle_...
53 RelDiff_NN3-001 RelDiff_NN3-001_LinearTrend_residue_bestCycle_...
54 RelDiff_NN3-001 RelDiff_NN3-001_Lag1Trend_residue_bestCycle_by...
55 Diff_NN3-001 Diff_NN3-001_PolyTrend_residue_bestCycle_byL2_...
56 Diff_NN3-001 Diff_NN3-001_Lag1Trend_residue_zeroCycle_resid...
57 Diff_NN3-001 Diff_NN3-001_Lag1Trend_residue_bestCycle_byL2_...
58 Diff_NN3-001 Diff_NN3-001_PolyTrend_residue_zeroCycle_resid...
59 Diff_NN3-001 Diff_NN3-001_PolyTrend_residue_zeroCycle_resid...
60 CumSum_NN3-001 CumSum_NN3-001_PolyTrend_residue_zeroCycle_res...
61 CumSum_NN3-001 CumSum_NN3-001_PolyTrend_residue_bestCycle_byL...
62 CumSum_NN3-001 CumSum_NN3-001_ConstantTrend_residue_zeroCycle...
63 CumSum_NN3-001 CumSum_NN3-001_ConstantTrend_residue_bestCycle...
Complexity FitCount FitL2 FitMAPE ForecastCount ForecastL2 \
0 0 52 723.815575 0.1043 13 441.319339
1 8 52 692.791329 0.0983 13 442.444672
2 24 52 558.010536 0.0769 13 480.712805
3 16 52 604.446093 0.0838 13 505.929016
4 64 52 808.218853 0.1166 13 539.194474
5 40 52 859.561626 0.1199 13 538.788659
6 32 52 896.183146 0.1267 13 523.391308
7 64 52 1026.482980 0.1382 13 523.391308
8 64 52 896.183146 0.1267 13 523.391308
9 64 52 896.183146 0.1267 13 523.391308
10 40 52 798.819140 0.1054 13 543.356509
11 64 52 851.431173 0.1191 13 578.706342
12 80 52 1108.700799 0.1505 13 600.269967
13 48 52 1108.362645 0.1503 13 616.734856
14 48 52 611.216896 0.0863 13 611.438259
15 56 52 585.897224 0.0821 13 627.418172
16 72 52 1281.131010 0.1467 13 681.624884
17 72 52 1285.447272 0.1811 13 712.026809
18 72 52 1103.451077 0.1537 13 775.879625
19 24 52 592.263885 0.0816 13 743.886065
20 72 52 656.523407 0.0911 13 802.615799
21 24 52 592.047757 0.0816 13 755.498898
22 40 52 512.667358 0.0705 13 754.881368
23 16 52 625.197334 0.0867 13 737.906817
24 40 52 512.787814 0.0705 13 761.446925
25 32 52 914.779970 0.1183 13 784.891778
26 16 52 625.007223 0.0868 13 749.113956
27 56 52 2962.792277 0.2959 13 849.096303
28 32 52 569.941980 0.0798 13 786.769608
29 32 52 569.976257 0.0797 13 794.429220
.. ... ... ... ... ... ...
34 88 52 849.657238 0.1175 13 1215.700440
35 48 52 735.525574 0.1001 13 1229.692045
36 64 52 1358.317337 0.2146 13 1256.234625
37 80 52 1029.965182 0.1490 13 1317.913614
38 72 52 1300.761779 0.2002 13 1342.904033
39 56 52 3373.840104 0.2962 13 1843.341414
40 72 52 1061.006296 0.1376 13 1462.417651
41 56 52 698.949903 0.0947 13 1460.820317
42 48 52 937.498560 0.1259 13 1543.005577
43 48 52 1357.819764 0.2113 13 1548.472289
44 56 52 1333.493038 0.2037 13 1562.698082
45 72 52 883.041109 0.1246 13 1741.081950
46 32 52 1197.291011 0.1794 13 1609.968944
47 40 52 1162.288352 0.1720 13 1609.968944
48 48 52 1064.009717 0.1527 13 1609.968944
49 48 52 1362.583992 0.2126 13 1609.968944
50 56 52 1009.276511 0.1442 13 1609.968944
51 56 52 1334.974010 0.2048 13 1609.968944
52 64 52 1365.650404 0.2168 13 1609.968944
53 72 52 1288.982144 0.2004 13 1609.968944
54 88 52 1144.173551 0.1721 13 1609.968944
55 56 52 627.613672 0.0860 13 1933.138028
56 80 52 1241.538636 0.1654 13 1985.420983
57 72 52 1242.286669 0.1615 13 1971.206858
58 48 52 682.099734 0.0944 13 2243.167395
59 64 52 1746.270610 0.2632 13 3662.480233
60 48 52 5009.131089 0.3759 13 4897.878173
61 56 52 5043.364512 0.4201 13 5326.791405
62 32 52 21434.457906 1.4976 13 6086.552643
63 40 52 22295.162361 1.5560 13 9547.146543
ForecastMAPE TestCount TestL2 TestMAPE
0 0.0557 2 295.785548 0.0377
1 0.0639 2 242.937430 0.0317
2 0.0670 2 321.864140 0.0518
3 0.0708 2 82.201198 0.0121
4 0.0719 2 547.391776 0.0763
5 0.0721 2 526.192074 0.0767
6 0.0722 2 500.399840 0.0808
7 0.0722 2 500.399840 0.0808
8 0.0722 2 500.399840 0.0808
9 0.0722 2 500.399840 0.0808
10 0.0772 2 418.587254 0.0632
11 0.0776 2 576.551145 0.0931
12 0.0809 2 662.202024 0.1061
13 0.0848 2 621.849841 0.1005
14 0.0863 2 267.406467 0.0368
15 0.0865 2 528.888613 0.0835
16 0.0902 2 1050.976917 0.1637
17 0.0976 2 255.094740 0.0335
18 0.1006 2 668.183568 0.1060
19 0.1044 2 588.929090 0.0936
20 0.1052 2 581.381547 0.0863
21 0.1063 2 623.289276 0.0993
22 0.1067 2 167.756994 0.0228
23 0.1073 2 651.122199 0.0990
24 0.1077 2 190.068076 0.0271
25 0.1078 2 819.052312 0.1252
26 0.1088 2 684.132101 0.1046
27 0.1139 2 639.735605 0.1019
28 0.1163 2 575.888147 0.0917
29 0.1175 2 606.714113 0.0968
.. ... ... ... ...
34 0.1796 2 1777.838880 0.2874
35 0.1806 2 1796.004838 0.2875
36 0.1880 2 723.499749 0.1145
37 0.2065 2 882.654204 0.1409
38 0.2124 2 465.251688 0.0640
39 0.2244 2 1666.582482 0.1887
40 0.2260 2 1750.927921 0.2725
41 0.2310 2 1260.255281 0.1997
42 0.2348 2 2272.599321 0.3653
43 0.2518 2 484.171584 0.0775
44 0.2540 2 532.841146 0.0860
45 0.2578 2 4524.690777 0.7309
46 0.2619 2 1450.000000 0.2329
47 0.2619 2 1450.000000 0.2329
48 0.2619 2 1450.000000 0.2329
49 0.2619 2 1450.000000 0.2329
50 0.2619 2 1450.000000 0.2329
51 0.2619 2 1450.000000 0.2329
52 0.2619 2 1450.000000 0.2329
53 0.2619 2 1450.000000 0.2329
54 0.2619 2 1450.000000 0.2329
55 0.2803 2 3822.194151 0.6186
56 0.2950 2 2042.484205 0.3307
57 0.3111 2 2341.655593 0.3677
58 0.3319 2 4204.303771 0.6797
59 0.4981 2 7827.437296 1.2660
60 0.8025 2 6345.347643 1.0273
61 0.8425 2 5364.191487 0.8227
62 1.0000 2 6194.650918 1.0000
63 1.0182 2 10511.629892 1.1615
[64 rows x 12 columns]
Transformation Model \
0 _NN3-001 _NN3-001_ConstantTrend_residue_zeroCycle_resid...
1 _NN3-001 _NN3-001_ConstantTrend_residue_bestCycle_byL2_...
2 _NN3-001 _NN3-001_ConstantTrend_residue_bestCycle_byL2_...
3 _NN3-001 _NN3-001_ConstantTrend_residue_zeroCycle_resid...
4 CumSum_NN3-001 CumSum_NN3-001_LinearTrend_residue_zeroCycle_r...
Complexity FitCount FitL2 FitMAPE ForecastCount ForecastL2 \
0 0 52 723.815575 0.1043 13 441.319339
1 8 52 692.791329 0.0983 13 442.444672
2 24 52 558.010536 0.0769 13 480.712805
3 16 52 604.446093 0.0838 13 505.929016
4 64 52 808.218853 0.1166 13 539.194474
ForecastMAPE TestCount TestL2 TestMAPE
0 0.0557 2 295.785548 0.0377
1 0.0639 2 242.937430 0.0317
2 0.0670 2 321.864140 0.0518
3 0.0708 2 82.201198 0.0121
4 0.0719 2 547.391776 0.0763
2 0 5060.0
1 5400.0
Name: NN3-001, dtype: float64
2 0 6017.115385
1 6017.115385
Name: NN3-001_Forecast, dtype: float64
FORECAST_DETAIL_ACTUAL NN3_Part1 NN3-001 [ 5060. 5400.]
FORECAST_DETAIL_PREDICTED NN3_Part1 NN3-001 [ 6017.11538462 6017.11538462]
BENCHMARK_PERF_DETAIL NN3_Part1 NN3-001 67 2 2.062260389328003 ConstantTrend + NoCycle + NoAR 2 0.1517 0.0702
In [ ]:
Content source: antoinecarme/pyaf
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