File: t_BoxCoxTransform_std.expout

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myBoxCox= BoxCox(lambda=[0.2], shift=[0])
myBoxCox input dimension =  1
myBoxCox output dimension =  1
input time series =
      [            marginal 1 ]
  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 = 
      [          y        ]
  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 ]
myInverseBoxCox =  InverseBoxCox(lambda=[0.2], shift=[0])
number of call(s) :  101