add notes regarding real world gotchas
This commit is contained in:
parent
c0a018ed26
commit
eb5d3bf8c5
39
README.md
39
README.md
@ 1,14 +1,47 @@


## kShape


# kShape




Python implementation of [kShape](http://www.cs.columbia.edu/~jopa/kshape.html),


a new fast and accurate unsupervised Time Series cluster algorithm




### Usage


## Usage




```


from kshape import kshape, zscore




time_series = [[1,2,3,4], [0,1,2,3], [1,1,1,1], [1,2,2,3]]


time_series = [[1,2,3,4], [0,1,2,3], [0,1,2,3], [1,2,2,3]]


cluster_num = 2


clusters = kshape(zscore(time_series), cluster_num)


#=> [(array([0.42860026, 1.15025211, 1.38751707, 0.42860026, 0.61993557]), [3]),


# (array([1.56839539, 0.40686255, 0.84042433, 0.67778452, 0.45704908]), [0, 1, 2])]


```




Returns list of tuples with the clusters found by kshape. The first value of the


tuple is zscore normalized centroid. The second value of the tuple is the index


of assigned series to this cluster.


The results can be examined by drawing graphs of the zscore normalized values


n/aand the corresponding centroid.




## Gotchas when working with realworld time series




 If the data is available from different sources with same frequency but at different points in time, it needs to be aligned.


 In the following a tab seperated file is assumed, where each column is a different observation;


gapps in columns happen, when only a certain value at this point in time was obtained.




```


import pandas as pd


# assuming the time series are stored in a tab seperated file, where `time` is


# the name of the column containing the timestamp


df = pd.read_csv(filename, sep="\t", index_col='time', parse_dates=True)


df = df.fillna(method="bfill", limit=1e9)


# drop rows with the same time stamp


df = df.groupby(level=0).first()


```




 kshape also expect no time series with a constant observation value or 'n/a'




```


time_series = []


for f in df.columns:


if not df[f].isnull().any() and df[f].var() != 0:


time_series.append[df[f]]


```



@ 1,6 +1,6 @@


from kshape import kshape, zscore




time_series = [[1,2,3,4], [0,1,2,3], [1,1,1,1], [1,2,2,3]]


time_series = [[1,2,3,4,5], [0,1,2,3,4], [3,2,1,0,1], [1,2,2,3,3]]


cluster_num = 2


clusters = kshape(zscore(time_series), cluster_num)


print(clusters)



Loading…
Reference in New Issue
Block a user