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|
myBoxCox= BoxCox(lambda=[0.2], shift=[0])
myBoxCox input dimension = 1
myBoxCox output dimension = 1
input time series =
[ t X0 ]
0 : [ 0 11.8246 ]
1 : [ 0.1 6.20148 ]
2 : [ 0.2 8.6852 ]
3 : [ 0.3 13.6164 ]
4 : [ 0.4 3.45584 ]
5 : [ 0.5 11.0501 ]
6 : [ 0.6 8.93498 ]
7 : [ 0.7 14.3117 ]
8 : [ 0.8 12.432 ]
9 : [ 0.9 12.3795 ]
10 : [ 1 8.58842 ]
11 : [ 1.1 10.7831 ]
12 : [ 1.2 3.12981 ]
13 : [ 1.3 6.15134 ]
14 : [ 1.4 6.06466 ]
15 : [ 1.5 9.72765 ]
16 : [ 1.6 12.9874 ]
17 : [ 1.7 9.58164 ]
18 : [ 1.8 8.31938 ]
19 : [ 1.9 11.3365 ]
20 : [ 2 10.9688 ]
21 : [ 2.1 11.3374 ]
22 : [ 2.2 6.88577 ]
23 : [ 2.3 7.42986 ]
24 : [ 2.4 11.4209 ]
25 : [ 2.5 9.62351 ]
26 : [ 2.6 11.0543 ]
27 : [ 2.7 15.3471 ]
28 : [ 2.8 10.2106 ]
29 : [ 2.9 7.6559 ]
30 : [ 3 7.8354 ]
31 : [ 3.1 9.27633 ]
32 : [ 3.2 4.63611 ]
33 : [ 3.3 11.2041 ]
34 : [ 3.4 14.1035 ]
35 : [ 3.5 13.013 ]
36 : [ 3.6 12.2246 ]
37 : [ 3.7 9.86916 ]
38 : [ 3.8 11.618 ]
39 : [ 3.9 10.8999 ]
40 : [ 4 11.2232 ]
41 : [ 4.1 8.54466 ]
42 : [ 4.2 8.85102 ]
43 : [ 4.3 7.74155 ]
44 : [ 4.4 10.7738 ]
45 : [ 4.5 15.9063 ]
46 : [ 4.6 7.98613 ]
47 : [ 4.7 15.5674 ]
48 : [ 4.8 10.1565 ]
49 : [ 4.9 12.3713 ]
50 : [ 5 12.1491 ]
51 : [ 5.1 7.76913 ]
52 : [ 5.2 10.5531 ]
53 : [ 5.3 5.4078 ]
54 : [ 5.4 11.9651 ]
55 : [ 5.5 11.6142 ]
56 : [ 5.6 15.2146 ]
57 : [ 5.7 7.12383 ]
58 : [ 5.8 11.1338 ]
59 : [ 5.9 9.45699 ]
60 : [ 6 15.0189 ]
61 : [ 6.1 6.88312 ]
62 : [ 6.2 8.93934 ]
63 : [ 6.3 13.6414 ]
64 : [ 6.4 7.6689 ]
65 : [ 6.5 5.89441 ]
66 : [ 6.6 10.3104 ]
67 : [ 6.7 7.32454 ]
68 : [ 6.8 12.7168 ]
69 : [ 6.9 11.0044 ]
70 : [ 7 8.54908 ]
71 : [ 7.1 12.0339 ]
72 : [ 7.2 15.1281 ]
73 : [ 7.3 13.2119 ]
74 : [ 7.4 8.47923 ]
75 : [ 7.5 5.01741 ]
76 : [ 7.6 16.7387 ]
77 : [ 7.7 12.2788 ]
78 : [ 7.8 8.46771 ]
79 : [ 7.9 8.1008 ]
80 : [ 8 7.12878 ]
81 : [ 8.1 11.6321 ]
82 : [ 8.2 12.4437 ]
83 : [ 8.3 7.79587 ]
84 : [ 8.4 9.66562 ]
85 : [ 8.5 12.9834 ]
86 : [ 8.6 9.51812 ]
87 : [ 8.7 7.18369 ]
88 : [ 8.8 4.09393 ]
89 : [ 8.9 8.02719 ]
90 : [ 9 11.0163 ]
91 : [ 9.1 13.0467 ]
92 : [ 9.2 11.9115 ]
93 : [ 9.3 9.73028 ]
94 : [ 9.4 7.43234 ]
95 : [ 9.5 13.8138 ]
96 : [ 9.6 9.28524 ]
97 : [ 9.7 13.9789 ]
98 : [ 9.8 16.359 ]
99 : [ 9.9 7.29526 ]
100 : [ 10 5.44911 ]
output time series =
[ t y0 ]
0 : [ 0 3.19459 ]
1 : [ 0.1 2.20226 ]
2 : [ 0.2 2.70417 ]
3 : [ 0.3 3.42913 ]
4 : [ 0.4 1.40739 ]
5 : [ 0.5 3.08432 ]
6 : [ 0.6 2.74798 ]
7 : [ 0.7 3.51351 ]
8 : [ 0.8 3.2771 ]
9 : [ 0.9 3.27009 ]
10 : [ 1 2.68693 ]
11 : [ 1.1 3.04486 ]
12 : [ 1.2 1.28165 ]
13 : [ 1.3 2.19058 ]
14 : [ 1.4 2.1702 ]
15 : [ 1.5 2.88082 ]
16 : [ 1.6 3.34977 ]
17 : [ 1.7 2.85702 ]
18 : [ 1.8 2.63815 ]
19 : [ 1.9 3.12579 ]
20 : [ 2 3.07238 ]
21 : [ 2.1 3.12592 ]
22 : [ 2.2 2.35462 ]
23 : [ 2.3 2.46734 ]
24 : [ 2.4 3.13785 ]
25 : [ 2.5 2.86388 ]
26 : [ 2.6 3.08492 ]
27 : [ 2.7 3.63327 ]
28 : [ 2.8 2.95757 ]
29 : [ 2.9 2.51224 ]
30 : [ 3 2.54714 ]
31 : [ 3.1 2.8063 ]
32 : [ 3.2 1.79518 ]
33 : [ 3.3 3.10672 ]
34 : [ 3.4 3.48858 ]
35 : [ 3.5 3.35306 ]
36 : [ 3.6 3.2493 ]
37 : [ 3.7 2.90362 ]
38 : [ 3.8 3.16576 ]
39 : [ 3.9 3.06221 ]
40 : [ 4 3.10948 ]
41 : [ 4.1 2.67908 ]
42 : [ 4.2 2.73337 ]
43 : [ 4.3 2.52897 ]
44 : [ 4.4 3.04347 ]
45 : [ 4.5 3.69528 ]
46 : [ 4.6 2.57595 ]
47 : [ 4.7 3.65791 ]
48 : [ 4.8 2.94911 ]
49 : [ 4.9 3.26901 ]
50 : [ 5 3.23908 ]
51 : [ 5.1 2.53433 ]
52 : [ 5.2 3.01024 ]
53 : [ 5.3 2.00768 ]
54 : [ 5.4 3.21397 ]
55 : [ 5.5 3.16522 ]
56 : [ 5.6 3.61831 ]
57 : [ 5.7 2.40479 ]
58 : [ 5.8 3.09652 ]
59 : [ 5.9 2.83647 ]
60 : [ 6 3.59602 ]
61 : [ 6.1 2.35406 ]
62 : [ 6.2 2.74874 ]
63 : [ 6.3 3.43222 ]
64 : [ 6.4 2.51479 ]
65 : [ 6.5 2.12948 ]
66 : [ 6.6 2.97306 ]
67 : [ 6.7 2.44605 ]
68 : [ 6.8 3.31468 ]
69 : [ 6.9 3.07761 ]
70 : [ 7 2.67987 ]
71 : [ 7.1 3.22339 ]
72 : [ 7.2 3.60849 ]
73 : [ 7.3 3.37843 ]
74 : [ 7.4 2.66728 ]
75 : [ 7.5 1.90345 ]
76 : [ 7.6 3.78444 ]
77 : [ 7.7 3.2566 ]
78 : [ 7.8 2.66519 ]
79 : [ 7.9 2.59759 ]
80 : [ 8 2.40582 ]
81 : [ 8.1 3.16774 ]
82 : [ 8.2 3.27866 ]
83 : [ 8.3 2.53951 ]
84 : [ 8.4 2.87075 ]
85 : [ 8.5 3.34926 ]
86 : [ 8.6 2.84658 ]
87 : [ 8.7 2.41719 ]
88 : [ 8.8 1.62824 ]
89 : [ 8.9 2.58373 ]
90 : [ 9 3.07936 ]
91 : [ 9.1 3.35738 ]
92 : [ 9.2 3.2066 ]
93 : [ 9.3 2.88125 ]
94 : [ 9.4 2.46784 ]
95 : [ 9.5 3.45343 ]
96 : [ 9.6 2.8078 ]
97 : [ 9.7 3.47353 ]
98 : [ 9.8 3.74423 ]
99 : [ 9.9 2.44009 ]
100 : [ 10 2.01835 ]
gradient= [[ 1.7411 ]]
hessian= sheet #0
[[ -2.78576 ]]
myInverseBoxCox = InverseBoxCox(lambda=[0.2], shift=[0])
number of call(s) : 101
|
