Quick Start

Create a pytseries.core.TimeSeries object

>>> from pytseries.core import TimeSeries
>>> 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")

Plot a pytseries.core.TimeSeries object

Note that we have interpolated our TimeSeries so that now we have 30 points.

>>> ts.plot()
_images/quick_start_plot1.png

Normalise and interpolate pytseries.core.TimeSeries object

>>> ts.interpolate(kind='linear', num=30, inplace=True)
>>> ts.norm(inplace=True)
>>> len(ts)
out: 30 ## now we have 30 data point
>> ts.max() ## now our min is 0 and max is 1
out: (180.0, 1.0)
>>> ts.plot()
_images/quick_start_plot2.png

Compute dynamic time warping distance between two pytseries.core.TimeSeries objects

>>> from pytseries.core import TimeSeries
>>> from pytseries.dtw import DTW
>>> 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')
>>> dtwxy = DTW(x=tsx, y=tsy)
>>> dtwxy
out: DTW(x=x, y=y, cost=0.4073)
>>> dtwxy.cost
out: 0.40730000000000044
>>> dtwxy.plot()
_images/quick_start_plot3.png 
>>> dtwxy.cost_plot()
_images/quick_start_plot4.png

Create a pytseries.core.TimeSeriesGroup Object

>>> 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]
>>> a_values = [i*3 for i in y_values]
>>> tsx = TimeSeries(time=time, values=x_values, feature='x')
>>> tsy = TimeSeries(time=time, values=y_values, feature='y')
>>> tsz = TimeSeries(time=time, values=z_values, feature='z')
>>> tsa = TimeSeries(time=time, values=a_values, feature='a')

Create a pytseries.core.TimeSeriesGroup using a list of pytseries.core.TimeSeries

>>> from pytseries.core import TimeSeriesGroup
>>> tsg = TimeSeriesGroup([tsx, tsy, tsz, tsa])
>>> 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
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
_images/quick_start_plot5.png

TimeSeriesGroup clustering

>>> from pytseries.clust import TimeSeriesKMeans
>>> c = TimeSeriesKMeans(tsg, n_clusters=2, max_iter=4)
>>> fig = c.plot_clusters()
Out: 4.913 --> 1.643 --> 1.643 -->
_images/quick_start_plot6.png _images/quick_start_plot7.png