The pytseries.core.TimeSeries Object¶
pytseries.core.TimeSeries¶
object for storing and manipulating pytseries data.
Create a time series object¶
>>> time = [15, 30, 60, 90, 120, 150, 180]
>>> x_values = [1, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459]
>>> ts = TimeSeries(time=time, values=x_values, feature='x')
>>> print(ts)
out: TimeSeries(data=[1.0, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459], \
time=[15, 30, 60, 90, 120, 150, 180], feature="x")
Perform numerical operations on TimeSeries¶
>>> time = [15, 30, 60, 90, 120, 150, 180]
>>> x_values = [1, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459]
>>> y_values = [0.989, 1.031, 1.233, 1.205, 1.158, 1.176, 1.204]
>>> tsx = TimeSeries(time=time, values=x_values, feature='x')
>>> tsy = TimeSeries(time=time, values=y_values, feature='y')
>>> tsx + tsy
Out: TimeSeries(data=[1.9889999999999999, 2.041, 2.277, 2.273, 2.254, 2.304, 2.3499], time=[15, 30, 60, 90, 120, 150, 180], feature=None)
>>> tsx * tsy
Out: TimeSeries(data=[0.989, 1.04131, 1.287252, 1.2869400000000002, 1.269168, 1.3265279999999997, 1.3796635999999998], time=[15, 30, 60, 90, 120, 150, 180], feature=None)
>>> tsx / tsy
Out: TimeSeries(data=[1.0111223458038423, 0.979631425800194, 0.8467153284671532, 0.8863070539419087, 0.9464594127806565, 0.9591836734693877, 0.9517441860465116], time=[15, 30, 60, 90, 120, 150, 180], feature=None)
>>> tsx - tsy
Out: TimeSeries(data=[0.01100000000000001, -0.020999999999999908, -0.18900000000000006, -0.137, -0.06199999999999983, -0.04800000000000004, -0.05810000000000004], time=[15, 30, 60, 90, 120, 150, 180], feature=None)
>>> tsx ** 2
Out: TimeSeries(data=[1.0, 1.0201, 1.089936, 1.140624, 1.2012160000000003, 1.2723839999999997, 1.3130868099999997], time=[15, 30, 60, 90, 120, 150, 180], feature=None)
Indexing operations¶
>>> tsx = TimeSeries(time=time, values=x_values, feature='x')
>>> tsx[15]
Out: 0.989
Distance Operations¶
>>> tsx.eucl_dist(tsy)
Out: 0.06457561
Collecting metrics¶
>>> tsx.max()
Out: (180, 1.1459)
>>> tsx.min()
Out: (15, 1.0)
Normalization¶
>>> tsx.norm(inplace=True)
Out: TimeSeries(data=[0.0, 0.0685, 0.3015, 0.466, 0.657, 0.877, 1.0], time=[15, 30, 60, 90, 120, 150, 180], feature="x")
Coercion¶
To numpy.array
>>> tsx.to_array()
Out: [[ 15. 1. ]
[ 30. 1.01 ]
[ 60. 1.044 ]
[ 90. 1.068 ]
[120. 1.096 ]
[150. 1.128 ]
[180. 1.1459]]
The pytseries.core.TimeSeriesGroup Object¶
Object for collecting a group of time series
Create TimeSeriesGroup object from list of TimeSeries¶
>>> time = [15, 30, 60, 90, 120, 150, 180]
>>> x_values = [1, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459]
>>> y_values = [0.989, 1.031, 1.233, 1.205, 1.158, 1.176, 1.204]
>>> tsx = TimeSeries(time=time, values=x_values, feature='x')
>>> tsy = TimeSeries(time=time, values=y_values, feature='y')
>>> tsg = TimeSeriesGroup([tsx, tsy])
out 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
Create TimeSeriesGroup object from list of pandas.DataFrame¶
>>> df = pandas.DataFrame([x_values, y_values], columns=time, index=['x', 'y'])
>>> tsg = TimeSeriesGroup(df)
>>> tsg
15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
Append TimeSeries to TimeSeriesGroup¶
>>> z_values = [i*2 for i in y_values]
>>> tsz = TimeSeries(time=time, values=z_values, feature='z')
>>> tsg.append(tsz, inplace=True)
Out: 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
z 1.978 2.062 2.466 2.410 2.316 2.352 2.4080
Concatonate two TimeSeriesGroup objects¶
>>> a_values = [i*3 for i in y_values]
>>> tsa = TimeSeries(time=time, values=a_values, feature='a')
>>> tsg1 = TimeSeriesGroup([tsx, tsy])
>>> tsg2 = TimeSeriesGroup([tsa, tsz])
>>> tsg = tsg1.concat(tsg2)
Out: 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
a 2.967 3.093 3.699 3.615 3.474 3.528 3.6120
z 1.978 2.062 2.466 2.410 2.316 2.352 2.4080
Indexing a pytseries.core.TimeSeriesGroup¶
The pytseries.core.TimeSeriesGroup is designed to work like a pandas.DataFrame
>>> tsg.loc['x']
15 1.0000
30 1.0100
60 1.0440
90 1.0680
120 1.0960
150 1.1280
180 1.1459
Name: x, dtype: float64
Note
The loc indexer currently returns a pandas.Series object. It would make more sense for a TimeSeries to be returned and this will be changed in future versions.
>>> time = [15, 30, 60, 90, 120, 150, 180]
>>> x_values = [1, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459]
>>> y_values = [0.989, 1.031, 1.233, 1.205, 1.158, 1.176, 1.204]
>>> z_values = [i*2 for i in y_values]
>>> df = pandas.DataFrame([x_values, y_values, z_values], columns=time, index=['x', 'y', 'z'])
>>> tsg = TimeSeriesGroup(df)
>>> tsg
Out: 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
z 1.978 2.062 2.466 2.410 2.316 2.352 2.4080
>>> tsg.loc[['x', 'y']])
Out: 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
The pandas.DataFrame.iloc indexer works as expected
>>> tsg.iloc[[0, 1]]
Out: 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
Return the shape of a TimeSeriesGroup
>>> tsg.shape
(3, 7)
Calcualte statistics on TimeSeriesGroups¶
>>> time = [15, 30, 60, 90, 120, 150, 180]
>>> x_values = [1, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459]
>>> y_values = [0.989, 1.031, 1.233, 1.205, 1.158, 1.176, 1.204]
>>> z_values = [i*2 for i in y_values]
>>> df = pandas.DataFrame([x_values, y_values, z_values], columns=time, index=['x', 'y', 'z'])
>>> tsg = TimeSeriesGroup(df)
>>> tsg.mean
Out: TimeSeries(data=[1.32, 1.36, 1.58, 1.561, 1.52, 1.55, 1.58], time=[15, 30, 60, 90, 120, 150, 180], feature="mean")
>>> tsg.median
Out: TimeSeries(data=[1.48, 1.5465, 1.84, 1.8075, 1.736, 1.763, 1.806], time=[15, 30, 60, 90, 120, 150, 180], feature="median")
>>> tsg.sd
Out: TimeSeries(data=[0.8175, 0.859, 1.067, 1.031, 0.974, 0.986, 1.011], time=[15, 30, 60, 90, 120, 150, 180], feature="std")
>>> tsg.var
Out: TimeSeries(data=[0.6684, 0.738, 1.139, 1.063, 0.949, 0.972, 1.023], time=[15, 30, 60, 90, 120, 150, 180], feature="var")
>>> tsg.coeff_var
Out: TimeSeries(data=[0.471, 0.47, 0.505, 0.497, 0.484, 0.481, 0.483], time=[15, 30, 60, 90, 120, 150, 180], feature="std")
It is also possible to calculate statistics by passing a callable to TimeSeriesGroup.do_statistic()
>>> import numpy
>>> tsg.do_statistic(numpy.median)
Out: TimeSeries(data=[1.48, 1.5465, 1.84, 1.8075, 1.73, 1.76, 1.806], time=[15, 30, 60, 90, 120, 150, 180], feature="function")
Calculate the dynamic time warping distance matrix for current TimeSeriesGroup¶
>>> tsg.dtw_cost_matrix
Out: x y a z
x NaN 0.3963 16.4961 8.5001
y 0.3963 NaN 15.992 7.996
a 16.4961 15.992 NaN 7.996
z 8.5001 7.996 7.996 NaN
Warning
Takes a long time for large TimeSeriesGroups
Calculate the euclidean distance matrix for current TimeSeriesGroup¶
>>> tsg.eucl_dist_matrix()
Out: a x y z
a NaN 39.240745 36.747808 9.186952
x 39.240745 NaN 0.064576 10.465708
y 36.747808 0.064576 NaN 9.186952
z 9.186952 10.465708 9.186952 NaN
Compute the centroid time series using DTW distance.¶
The centroid is the time series in a TimeSeriesGroup that has minimum distance to all other time series in the group.
>>> tsg.centroid_by_dtw()
Out: TimeSeries(data=[0.989, 1.031, 1.233, 1.205, 1.158, 1.176, 1.204], time=[15, 30, 60, 90, 120, 150, 180], feature="y")
Compute centroid time series using euclidean distance¶
>>> tsg.centroid_by_eucl()
Out: TimeSeries(data=[1.978, 2.062, 2.466, 2.41, 2.316, 2.352, 2.408], time=[15, 30, 60, 90, 120, 150, 180], feature="z")
Coerce TimeSeriesGroup to numpy.array¶
>>> tsg.to_array()
[[[ 15. 1. ]
[ 30. 1.01 ]
[ 60. 1.044 ]
[ 90. 1.068 ]
[120. 1.096 ]
[150. 1.128 ]
[180. 1.1459]]
[[ 15. 0.989 ]
[ 30. 1.031 ]
[ 60. 1.233 ]
[ 90. 1.205 ]
[120. 1.158 ]
[150. 1.176 ]
[180. 1.204 ]]
[[ 15. 2.967 ]
[ 30. 3.093 ]
[ 60. 3.699 ]
[ 90. 3.615 ]
[120. 3.474 ]
[150. 3.528 ]
[180. 3.612 ]]
[[ 15. 1.978 ]
[ 30. 2.062 ]
[ 60. 2.466 ]
[ 90. 2.41 ]
[120. 2.316 ]
[150. 2.352 ]
[180. 2.408 ]]]
Coerce TimeSeriesGroup into a list of TimeSeries objects¶
>>> tsg.to_ts()
Out: [TimeSeries(data=[1.0, 1.01, 1.044, 1.068, 1.096, 1.128, 1.1459], time=[15, 30, 60, 90, 120, 150, 180], feature="x"),
TimeSeries(data=[0.989, 1.031, 1.233, 1.205, 1.158, 1.176, 1.204], time=[15, 30, 60, 90, 120, 150, 180], feature="y"),
TimeSeries(data=[2.967, 3.093, 3.6990000000000003, 3.615, 3.4739999999999998, 3.5279999999999996, 3.612], time=[15, 30, 60, 90, 120, 150, 180], feature="a"),
TimeSeries(data=[1.978, 2.062, 2.466, 2.41, 2.316, 2.352, 2.408], time=[15, 30, 60, 90, 120, 150, 180], feature="z")]
Coerce TimeSeriesGroup into pandas.DataFrame¶
>>> tsg.as_df()
Out: 15 30 60 90 120 150 180
x 1.000 1.010 1.044 1.068 1.096 1.128 1.1459
y 0.989 1.031 1.233 1.205 1.158 1.176 1.2040
a 2.967 3.093 3.699 3.615 3.474 3.528 3.6120
z 1.978 2.062 2.466 2.410 2.316 2.352 2.4080
Normalisation¶
>>> tsg.norm()
Out: 15 30 60 90 120 150 180
x 0.0 0.068540 0.301576 0.466073 0.657985 0.877313 1.000000
y 0.0 0.172131 1.000000 0.885246 0.692623 0.766393 0.881148
a 0.0 0.172131 1.000000 0.885246 0.692623 0.766393 0.881148
z 0.0 0.172131 1.000000 0.885246 0.692623 0.766393 0.881148
See TimeSeriesGroup.norm()
Interpolation¶
>>> tsg.interpolate('linear', num=10)
Out: 15.000000 33.333333 51.666667 70.000000 88.333333 106.666667 \
x 1.000 1.013778 1.034556 1.052000 1.066667 1.083556
y 0.989 1.053444 1.176889 1.223667 1.206556 1.178889
a 2.967 3.160333 3.530667 3.671000 3.619667 3.536667
z 1.978 2.106889 2.353778 2.447333 2.413111 2.357778
125.000000 143.333333 161.666667 180.000000
x 1.101333 1.120889 1.134961 1.1459
y 1.161000 1.172000 1.186889 1.2040
a 3.483000 3.516000 3.560667 3.6120
z 2.322000 2.344000 2.373778 2.4080
See TimeSeriesGroup.interpolation()
Differentiation¶
>>> tsg.dydt()
Out: 15 45 75 105 135 165
x 0.010 0.034 0.024 0.028 0.032 0.0179
y 0.042 0.202 -0.028 -0.047 0.018 0.0280
a 0.126 0.606 -0.084 -0.141 0.054 0.0840
z 0.084 0.404 -0.056 -0.094 0.036 0.0560
See TimeSeriesGroup.dydt()