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@ -6,7 +6,7 @@ a new fast and accurate unsupervised Time Series cluster algorithm.
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## Usage
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```
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```python
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from kshape import kshape, zscore
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time_series = [[1,2,3,4], [0,1,2,3], [0,1,2,3], [1,2,2,3]]
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@ -28,7 +28,7 @@ and the corresponding centroid.
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- In the following a tab seperated file is assumed, where each column is a different observation;
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gapps in columns happen, when only a certain value at this point in time was obtained.
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```
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```python
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import pandas as pd
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# assuming the time series are stored in a tab seperated file, where `time` is
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# the name of the column containing the timestamp
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@ -43,7 +43,7 @@ df.fillna(method="bfill", inplace=True)
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- kshape also expect no time series with a constant observation value or 'n/a'
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```
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```python
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time_series = []
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for f in df.columns:
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if not df[f].isnull().any() and df[f].var() != 0:
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@ -52,7 +52,7 @@ for f in df.columns:
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## Relevant Articles
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```
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```plain
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Paparrizos J and Gravano L (2015).
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k-Shape: Efficient and Accurate Clustering of Time Series.
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In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, series SIGMOD '15,
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