In [1]:
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
%matplotlib inline
In [ ]:
In [2]:
b1 = tsds.load_ozone_exogenous_categorical()
df = b1.mPastData
Date Month Exog2 Exog3 Exog4 Ozone Time Ozone2
0 1955-01 1955 1 AQ P_R 2.7 1955-01-01 2.7
1 1955-02 1955 2 AR P_R 2.0 1955-02-01 2.0
2 1955-03 1955 3 AS P_S 3.6 1955-03-01 3.6
3 1955-04 1955 4 AT P_U 5.0 1955-04-01 5.0
4 1955-05 1955 5 AU P_V 6.5 1955-05-01 6.5
In [3]:
df.head()
Out[3]:
Date
Month
Exog2
Exog3
Exog4
Ozone
Time
Ozone2
0
1955-01
1955
1
AQ
P_R
2.7
1955-01-01
2.7
1
1955-02
1955
2
AR
P_R
2.0
1955-02-01
2.0
2
1955-03
1955
3
AS
P_S
3.6
1955-03-01
3.6
3
1955-04
1955
4
AT
P_U
5.0
1955-04-01
5.0
4
1955-05
1955
5
AU
P_V
6.5
1955-05-01
6.5
In [4]:
df.describe(include=['category'])
Out[4]:
Exog2
Exog3
Exog4
count
204
204
204
unique
12
12
8
top
12
A\
P_S
freq
17
17
47
In [5]:
b1.mExogenousDataFrame.Exog4.cat.categories
Out[5]:
Index(['P_Q', 'P_R', 'P_S', 'P_T', 'P_U', 'P_V', 'P_W', 'P_X'], dtype='object')
In [6]:
import scipy
In [7]:
type(b1.mExogenousDataFrame.Exog3.dtype)
Out[7]:
pandas.core.dtypes.dtypes.CategoricalDtype
In [8]:
b1.mExogenousDataFrame.Exog3.dtype == "category"
Out[8]:
True
In [9]:
b1.mExogenousDataFrame.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 216 entries, 0 to 215
Data columns (total 8 columns):
Date 216 non-null object
Month 216 non-null int64
Exog2 216 non-null category
Exog3 216 non-null category
Exog4 216 non-null category
Ozone 216 non-null float64
Time 216 non-null datetime64[ns]
Ozone2 216 non-null float64
dtypes: category(3), datetime64[ns](1), float64(2), int64(1), object(1)
memory usage: 10.3+ KB
In [10]:
lEngine = autof.cForecastEngine()
lEngine.mOptions.mDebug = True;
#lEngine.mOptions.mDebugProfile = True;
lEngine.mOptions.disable_all_periodics()
lEngine.mOptions.set_active_autoregressions(['ARX'])
lExogenousData = (b1.mExogenousDataFrame , b1.mExogenousVariables)
lEngine
#lEngine.mOptions.enable_slow_mode()
#lEngine.mOptions.mCycle_Criterion = "L2";
#lEngine.mOptions.mCycle_Criterion_Threshold = 10000.2;
Out[10]:
<pyaf.ForecastEngine.cForecastEngine at 0x7fa0e17ee898>
In [11]:
lEngine.train(df , 'Time' , b1.mSignalVar, 12, lExogenousData)
INFO:pyaf.std:START_TRAINING 'Ozone2'
INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone2' 8.638344526290894
In [12]:
lEngine.getModelInfo()
INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta=30 days Estimation = (0 , 153) Validation = (153 , 192) Test = (192 , 204) Horizon=12
INFO:pyaf.std:SIGNAL_DETAIL SignalVariable='_Ozone2' Min=0.0 Max=26.1 Mean=5.54264705882 StdDev=3.82404606238
INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_'
INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' [ConstantTrend + NoCycle + ARX(51)]
INFO:pyaf.std:TREND_DETAIL '_Ozone2_ConstantTrend' [ConstantTrend]
INFO:pyaf.std:CYCLE_DETAIL '_Ozone2_ConstantTrend_residue_zeroCycle' [NoCycle]
INFO:pyaf.std:AUTOREG_DETAIL '_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' [ARX(51)]
INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1155540983.75 MAPE_Forecast=0.3059 MAPE_Test=0.2789
INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4148 MASE_Forecast=0.5234 MASE_Test=0.5512
INFO:pyaf.std:MODEL_L1 L1_Fit=1.74072132203 L1_Forecast=2.06542578835 L1_Test=0.982069337047
INFO:pyaf.std:MODEL_L2 L2_Fit=1.74072132203 L2_Forecast=2.06542578835 L2_Test=1.11755846411
INFO:pyaf.std:MODEL_COMPLEXITY 51
INFO:pyaf.std:AR_MODEL_DETAIL_START
INFO:pyaf.std:AR_MODEL_COEFF 1 Exog3=AT_Lag5 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 2 Exog2=3_Lag6 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 3 Exog2=2_Lag7 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 4 Exog2=4_Lag5 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 5 Exog2=5_Lag4 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 6 Exog3=AU_Lag4 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 7 Exog3=AR_Lag7 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 8 Exog3=AS_Lag6 1.6418815066
INFO:pyaf.std:AR_MODEL_COEFF 9 Exog3=AS_Lag42 -1.15781717492
INFO:pyaf.std:AR_MODEL_COEFF 10 Exog2=5_Lag40 -1.15781717492
INFO:pyaf.std:AR_MODEL_DETAIL_END
In [13]:
lEngine.mSignalDecomposition.mExogenousData
Out[13]:
( Date Month Exog2 Exog3 Exog4 Ozone Time Ozone2
0 1955-01 1955 1 AQ P_R 2.7 1955-01-01 2.7
1 1955-02 1955 2 AR P_R 2.0 1955-02-01 2.0
2 1955-03 1955 3 AS P_S 3.6 1955-03-01 3.6
3 1955-04 1955 4 AT P_U 5.0 1955-04-01 5.0
4 1955-05 1955 5 AU P_V 6.5 1955-05-01 6.5
5 1955-06 1955 6 AV P_V 6.1 1955-06-01 6.1
6 1955-07 1955 7 AW P_U 5.9 1955-07-01 5.9
7 1955-08 1955 8 AX P_U 5.0 1955-08-01 5.0
8 1955-09 1955 9 AY P_V 6.4 1955-09-01 19.2
9 1955-10 1955 10 AZ P_W 7.4 1955-10-01 7.4
10 1955-11 1955 11 A[ P_X 8.2 1955-11-01 8.2
11 1955-12 1955 12 A\ P_S 3.9 1955-12-01 3.9
12 1956-01 1956 1 AQ P_T 4.1 1956-01-01 8.2
13 1956-02 1956 2 AR P_T 4.5 1956-02-01 0.0
14 1956-03 1956 3 AS P_U 5.5 1956-03-01 0.0
15 1956-04 1956 4 AT P_S 3.8 1956-04-01 0.0
16 1956-05 1956 5 AU P_T 4.8 1956-05-01 9.6
17 1956-06 1956 6 AV P_U 5.6 1956-06-01 5.6
18 1956-07 1956 7 AW P_V 6.3 1956-07-01 6.3
19 1956-08 1956 8 AX P_U 5.9 1956-08-01 5.9
20 1956-09 1956 9 AY P_X 8.7 1956-09-01 26.1
21 1956-10 1956 10 AZ P_U 5.3 1956-10-01 5.3
22 1956-11 1956 11 A[ P_U 5.7 1956-11-01 5.7
23 1956-12 1956 12 A\ P_U 5.7 1956-12-01 5.7
24 1957-01 1957 1 AQ P_S 3.0 1957-01-01 3.0
25 1957-02 1957 2 AR P_S 3.4 1957-02-01 3.4
26 1957-03 1957 3 AS P_T 4.9 1957-03-01 4.9
27 1957-04 1957 4 AT P_T 4.5 1957-04-01 0.0
28 1957-05 1957 5 AU P_T 4.0 1957-05-01 4.0
29 1957-06 1957 6 AV P_U 5.7 1957-06-01 5.7
.. ... ... ... ... ... ... ... ...
186 1970-07 1970 7 AW P_S 3.8 1970-07-01 7.6
187 1970-08 1970 8 AX P_T 4.7 1970-08-01 9.4
188 1970-09 1970 9 AY P_T 4.6 1970-09-01 13.8
189 1970-10 1970 10 AZ P_R 2.9 1970-10-01 2.9
190 1970-11 1970 11 A[ P_Q 1.7 1970-11-01 3.4
191 1970-12 1970 12 A\ P_Q 1.3 1970-12-01 2.6
192 1971-01 1971 1 AQ P_Q 1.8 1971-01-01 3.6
193 1971-02 1971 2 AR P_R 2.0 1971-02-01 2.0
194 1971-03 1971 3 AS P_R 2.2 1971-03-01 2.2
195 1971-04 1971 4 AT P_S 3.0 1971-04-01 3.0
196 1971-05 1971 5 AU P_R 2.4 1971-05-01 4.8
197 1971-06 1971 6 AV P_S 3.5 1971-06-01 7.0
198 1971-07 1971 7 AW P_S 3.5 1971-07-01 7.0
199 1971-08 1971 8 AX P_S 3.3 1971-08-01 6.6
200 1971-09 1971 9 AY P_R 2.7 1971-09-01 10.8
201 1971-10 1971 10 AZ P_R 2.5 1971-10-01 5.0
202 1971-11 1971 11 A[ P_Q 1.6 1971-11-01 3.2
203 1971-12 1971 12 A\ P_Q 1.2 1971-12-01 2.4
204 1972-01 1972 1 AQ P_Q 1.5 1972-01-01 3.0
205 1972-02 1972 2 AR P_R 2.0 1972-02-01 2.0
206 1972-03 1972 3 AS P_S 3.1 1972-03-01 3.1
207 1972-04 1972 4 AT P_S 3.0 1972-04-01 3.0
208 1972-05 1972 5 AU P_S 3.5 1972-05-01 7.0
209 1972-06 1972 6 AV P_S 3.4 1972-06-01 6.8
210 1972-07 1972 7 AW P_T 4.0 1972-07-01 8.0
211 1972-08 1972 8 AX P_S 3.8 1972-08-01 3.8
212 1972-09 1972 9 AY P_S 3.1 1972-09-01 12.4
213 1972-10 1972 10 AZ P_R 2.1 1972-10-01 4.2
214 1972-11 1972 11 A[ P_Q 1.6 1972-11-01 3.2
215 1972-12 1972 12 A\ P_Q 1.3 1972-12-01 2.6
[216 rows x 8 columns], ['Exog2', 'Exog3', 'Exog4'])
In [14]:
type1 = np.dtype(df.Time)
In [15]:
type1.kind
Out[15]:
'M'
In [16]:
lEngine.mSignalDecomposition.mTrPerfDetails
Out[16]:
Transformation
Model
Complexity
FitCount
FitL1
FitL2
FitMAPE
FitMASE
ForecastCount
ForecastL1
ForecastL2
ForecastMAPE
ForecastMASE
TestCount
TestL1
TestL2
TestMAPE
TestMASE
0
_Ozone2
_Ozone2_LinearTrend_residue_zeroCycle_residue_...
67
153
1.344360e+00
1.737303e+00
1.173623e+09
4.136000e-01
39
1.474441e+00
2.072040e+00
2.960000e-01
5.188000e-01
12
9.035044e-01
1.054115e+00
2.556000e-01
5.071000e-01
1
_Ozone2
_Ozone2_ConstantTrend_residue_zeroCycle_residu...
51
153
1.348134e+00
1.740721e+00
1.155541e+09
4.148000e-01
39
1.487502e+00
2.065426e+00
3.059000e-01
5.234000e-01
12
9.820693e-01
1.117558e+00
2.789000e-01
5.512000e-01
2
_Ozone2
_Ozone2_PolyTrend_residue_zeroCycle_residue_AR...
67
153
1.343746e+00
1.736364e+00
1.166802e+09
4.135000e-01
39
1.534451e+00
2.066438e+00
3.319000e-01
5.399000e-01
12
1.170235e+00
1.286508e+00
3.359000e-01
6.568000e-01
3
CumSum_Ozone2
CumSum_Ozone2_Lag1Trend_residue_zeroCycle_resi...
115
153
1.779672e+00
2.416111e+00
1.228734e+09
5.476000e-01
39
2.257540e+00
3.172179e+00
4.432000e-01
7.943000e-01
12
1.354663e+00
1.650164e+00
2.967000e-01
7.603000e-01
4
_Ozone2
_Ozone2_Lag1Trend_residue_zeroCycle_residue_AR...
83
153
1.836490e+00
2.532612e+00
1.261014e+09
5.651000e-01
39
2.456359e+00
3.604933e+00
4.778000e-01
8.643000e-01
12
1.841065e+00
2.482662e+00
4.057000e-01
1.033300e+00
5
Diff_Ozone2
Diff_Ozone2_PolyTrend_residue_zeroCycle_residu...
99
153
5.335368e+00
5.852276e+00
1.142074e+09
1.641700e+00
39
2.895397e+00
3.671124e+00
5.871000e-01
1.018800e+00
12
5.007064e+00
5.386804e+00
1.411300e+00
2.810100e+00
6
CumSum_Ozone2
CumSum_Ozone2_PolyTrend_residue_zeroCycle_resi...
99
153
2.224329e+00
3.113178e+00
9.287929e+08
6.844000e-01
39
2.897290e+00
3.891538e+00
5.980000e-01
1.019400e+00
12
1.616549e+00
2.029560e+00
3.554000e-01
9.072000e-01
7
CumSum_Ozone2
CumSum_Ozone2_LinearTrend_residue_zeroCycle_re...
99
153
2.192795e+00
3.048176e+00
8.947320e+08
6.747000e-01
39
3.211988e+00
4.500115e+00
6.326000e-01
1.130100e+00
12
2.261574e+00
2.677425e+00
4.949000e-01
1.269300e+00
8
CumSum_Ozone2
CumSum_Ozone2_ConstantTrend_residue_zeroCycle_...
83
153
2.353848e+00
3.341358e+00
1.092178e+09
7.243000e-01
39
3.165114e+00
4.473438e+00
6.339000e-01
1.113700e+00
12
1.915967e+00
2.535174e+00
4.221000e-01
1.075300e+00
9
Diff_Ozone2
Diff_Ozone2_LinearTrend_residue_zeroCycle_resi...
99
153
5.570456e+00
5.987756e+00
1.074544e+09
1.714000e+00
39
3.921202e+00
4.627036e+00
7.465000e-01
1.379700e+00
12
1.994069e+00
2.424893e+00
3.827000e-01
1.119100e+00
10
Diff_Ozone2
Diff_Ozone2_Lag1Trend_residue_zeroCycle_residu...
115
153
1.641996e+01
1.881203e+01
3.078581e+09
5.052300e+00
39
3.475029e+00
4.381080e+00
7.678000e-01
1.222700e+00
12
7.461257e+00
7.686989e+00
1.946900e+00
4.187400e+00
11
Diff_Ozone2
Diff_Ozone2_ConstantTrend_residue_zeroCycle_re...
83
153
4.607965e+00
5.031361e+00
1.027263e+09
1.417800e+00
39
4.858505e+00
5.388206e+00
9.708000e-01
1.709500e+00
12
3.996849e+00
4.383744e+00
9.784000e-01
2.243100e+00
12
RelDiff_Ozone2
RelDiff_Ozone2_ConstantTrend_residue_zeroCycle...
83
153
2.682369e+08
2.691162e+08
8.823529e+16
8.253444e+07
39
2.700000e+08
2.700000e+08
6.472450e+07
9.500000e+07
12
2.700000e+08
2.700000e+08
7.274202e+07
1.515306e+08
13
RelDiff_Ozone2
RelDiff_Ozone2_LinearTrend_residue_zeroCycle_r...
99
153
2.682358e+08
2.691162e+08
8.823529e+16
8.253410e+07
39
2.700000e+08
2.700000e+08
6.472450e+07
9.500000e+07
12
2.700000e+08
2.700000e+08
7.274202e+07
1.515306e+08
14
RelDiff_Ozone2
RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_res...
115
153
2.682359e+08
2.691162e+08
8.823529e+16
8.253411e+07
39
2.700000e+08
2.700000e+08
6.472450e+07
9.500000e+07
12
2.700000e+08
2.700000e+08
7.274202e+07
1.515306e+08
15
RelDiff_Ozone2
RelDiff_Ozone2_PolyTrend_residue_zeroCycle_res...
99
153
2.682363e+08
2.691162e+08
8.823529e+16
8.253424e+07
39
2.700000e+08
2.700000e+08
6.472450e+07
9.500000e+07
12
2.700000e+08
2.700000e+08
7.274202e+07
1.515306e+08
In [17]:
lEngine.standrdPlots()
INFO:pyaf.std:START_PLOTTING
/home/antoine/.local/lib/python3.5/site-packages/matplotlib/__init__.py:1405: UserWarning:
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.
warnings.warn(_use_error_msg)
/home/antoine/.local/lib/python3.5/site-packages/matplotlib/__init__.py:1405: UserWarning:
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.
warnings.warn(_use_error_msg)
INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 5.7034502029418945
In [18]:
lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mTimeDelta
Out[18]:
numpy.timedelta64(30,'D')
In [19]:
dfapp = df.copy();
In [20]:
dfapp.head()
Out[20]:
Date
Month
Exog2
Exog3
Exog4
Ozone
Time
Ozone2
0
1955-01
1955
1
AQ
P_R
2.7
1955-01-01
2.7
1
1955-02
1955
2
AR
P_R
2.0
1955-02-01
2.0
2
1955-03
1955
3
AS
P_S
3.6
1955-03-01
3.6
3
1955-04
1955
4
AT
P_U
5.0
1955-04-01
5.0
4
1955-05
1955
5
AU
P_V
6.5
1955-05-01
6.5
In [ ]:
In [21]:
dfapp1 = lEngine.forecast(dfapp, 36);
INFO:pyaf.std:START_FORECASTING
INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 13.56264328956604
In [22]:
dfapp1.head()
Out[22]:
Ozone2
Time
_Ozone2
row_number
Time_Normalized
_Ozone2_ConstantTrend
_Ozone2_ConstantTrend_residue
_Ozone2_ConstantTrend_residue_zeroCycle
_Ozone2_ConstantTrend_residue_zeroCycle_residue
Exog2=1
...
_Ozone2_Cycle
_Ozone2_Cycle_residue
_Ozone2_AR
_Ozone2_AR_residue
_Ozone2_TransformedForecast
_Ozone2_TransformedResidue
Ozone2_Forecast
Ozone2_Residue
Ozone2_Forecast_Lower_Bound
Ozone2_Forecast_Upper_Bound
0
2.7
1955-01-01
2.7
0
-1.183208
5.579085
-2.879085
0.0
-2.879085
1.0
...
0.0
-2.879085
-0.852483
-2.026602
4.726602
-2.026602
4.726602
-2.026602
NaN
NaN
1
2.0
1955-02-01
2.0
1
-1.176507
5.579085
-3.579085
0.0
-3.579085
0.0
...
0.0
-3.579085
-0.852483
-2.726602
4.726602
-2.726602
4.726602
-2.726602
NaN
NaN
2
3.6
1955-03-01
3.6
2
-1.170454
5.579085
-1.979085
0.0
-1.979085
0.0
...
0.0
-1.979085
-0.852483
-1.126602
4.726602
-1.126602
4.726602
-1.126602
NaN
NaN
3
5.0
1955-04-01
5.0
3
-1.163753
5.579085
-0.579085
0.0
-0.579085
0.0
...
0.0
-0.579085
-0.852483
0.273398
4.726602
0.273398
4.726602
0.273398
NaN
NaN
4
6.5
1955-05-01
6.5
4
-1.157268
5.579085
0.920915
0.0
0.920915
0.0
...
0.0
0.920915
-0.852483
1.773398
4.726602
1.773398
4.726602
1.773398
NaN
NaN
5 rows × 38 columns
In [23]:
dfapp1.tail(20)
Out[23]:
Ozone2
Time
_Ozone2
row_number
Time_Normalized
_Ozone2_ConstantTrend
_Ozone2_ConstantTrend_residue
_Ozone2_ConstantTrend_residue_zeroCycle
_Ozone2_ConstantTrend_residue_zeroCycle_residue
Exog2=1
...
_Ozone2_Cycle
_Ozone2_Cycle_residue
_Ozone2_AR
_Ozone2_AR_residue
_Ozone2_TransformedForecast
_Ozone2_TransformedResidue
Ozone2_Forecast
Ozone2_Residue
Ozone2_Forecast_Lower_Bound
Ozone2_Forecast_Upper_Bound
220
NaN
1973-04-24
6.105751
220
0.262533
5.579085
0.526666
0.0
0.526666
0.0
...
0.0
0.526666
0.526666
2.220446e-16
6.105751
0.000000e+00
6.105751
0.000000e+00
NaN
NaN
221
NaN
1973-05-24
5.387501
221
0.269018
5.579085
-0.191584
0.0
-0.191584
0.0
...
0.0
-0.191584
-0.191584
-3.885781e-16
5.387501
0.000000e+00
5.387501
0.000000e+00
NaN
NaN
222
NaN
1973-06-23
6.541300
222
0.275503
5.579085
0.962215
0.0
0.962215
0.0
...
0.0
0.962215
0.962215
4.440892e-16
6.541300
0.000000e+00
6.541300
0.000000e+00
NaN
NaN
223
NaN
1973-07-23
7.047441
223
0.281989
5.579085
1.468356
0.0
1.468356
0.0
...
0.0
1.468356
1.468356
-2.220446e-16
7.047441
0.000000e+00
7.047441
0.000000e+00
NaN
NaN
224
NaN
1973-08-22
-9.470756
224
0.288474
5.579085
-15.049841
0.0
-15.049841
0.0
...
0.0
-15.049841
-15.049841
1.776357e-15
-9.470756
1.776357e-15
-9.470756
1.776357e-15
NaN
NaN
225
NaN
1973-09-21
4.927371
225
0.294959
5.579085
-0.651714
0.0
-0.651714
0.0
...
0.0
-0.651714
-0.651714
3.330669e-16
4.927371
0.000000e+00
4.927371
0.000000e+00
NaN
NaN
226
NaN
1973-10-21
4.407777
226
0.301444
5.579085
-1.171308
0.0
-1.171308
0.0
...
0.0
-1.171308
-1.171308
-2.220446e-16
4.407777
0.000000e+00
4.407777
0.000000e+00
NaN
NaN
227
NaN
1973-11-20
3.964705
227
0.307929
5.579085
-1.614379
0.0
-1.614379
0.0
...
0.0
-1.614379
-1.614379
2.220446e-16
3.964705
0.000000e+00
3.964705
0.000000e+00
NaN
NaN
228
NaN
1973-12-20
4.151206
228
0.314414
5.579085
-1.427879
0.0
-1.427879
0.0
...
0.0
-1.427879
-1.427879
-2.220446e-16
4.151206
0.000000e+00
4.151206
0.000000e+00
NaN
NaN
229
NaN
1974-01-19
3.559227
229
0.320899
5.579085
-2.019858
0.0
-2.019858
0.0
...
0.0
-2.019858
-2.019858
0.000000e+00
3.559227
0.000000e+00
3.559227
0.000000e+00
NaN
NaN
230
NaN
1974-02-18
3.763137
230
0.327384
5.579085
-1.815948
0.0
-1.815948
0.0
...
0.0
-1.815948
-1.815948
0.000000e+00
3.763137
0.000000e+00
3.763137
0.000000e+00
NaN
NaN
231
NaN
1974-03-20
4.090720
231
0.333869
5.579085
-1.488365
0.0
-1.488365
0.0
...
0.0
-1.488365
-1.488365
2.220446e-16
4.090720
0.000000e+00
4.090720
0.000000e+00
NaN
NaN
232
NaN
1974-04-19
5.809615
232
0.340354
5.579085
0.230530
0.0
0.230530
0.0
...
0.0
0.230530
0.230530
3.885781e-16
5.809615
0.000000e+00
5.809615
0.000000e+00
NaN
NaN
233
NaN
1974-05-19
5.604273
233
0.346839
5.579085
0.025188
0.0
0.025188
0.0
...
0.0
0.025188
0.025188
-1.110223e-16
5.604273
0.000000e+00
5.604273
0.000000e+00
NaN
NaN
234
NaN
1974-06-18
6.321990
234
0.353324
5.579085
0.742905
0.0
0.742905
0.0
...
0.0
0.742905
0.742905
-2.220446e-16
6.321990
0.000000e+00
6.321990
0.000000e+00
NaN
NaN
235
NaN
1974-07-18
6.649917
235
0.359810
5.579085
1.070832
0.0
1.070832
0.0
...
0.0
1.070832
1.070832
2.220446e-16
6.649917
0.000000e+00
6.649917
0.000000e+00
NaN
NaN
236
NaN
1974-08-17
-7.473334
236
0.366295
5.579085
-13.052419
0.0
-13.052419
0.0
...
0.0
-13.052419
-13.052419
-1.776357e-15
-7.473334
-1.776357e-15
-7.473334
-1.776357e-15
NaN
NaN
237
NaN
1974-09-16
4.641521
237
0.372780
5.579085
-0.937564
0.0
-0.937564
0.0
...
0.0
-0.937564
-0.937564
-2.220446e-16
4.641521
0.000000e+00
4.641521
0.000000e+00
NaN
NaN
238
NaN
1974-10-16
4.348407
238
0.379265
5.579085
-1.230678
0.0
-1.230678
0.0
...
0.0
-1.230678
-1.230678
2.220446e-16
4.348407
0.000000e+00
4.348407
0.000000e+00
NaN
NaN
239
NaN
1974-11-15
4.348407
239
0.385750
5.579085
-1.230678
0.0
-1.230678
0.0
...
0.0
-1.230678
-1.664262
4.335843e-01
3.914823
4.335843e-01
3.914823
4.335843e-01
NaN
NaN
20 rows × 38 columns
In [24]:
#trdec.mTimeInfo.mTimeDelta
Content source: antoinecarme/pyaf
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