Updating the links due to notebook refactoring
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@ -27,7 +27,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
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- which fuzzy membership function (on `pyFTS.common.Membership <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py>`_)
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- partition scheme (`GridPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py>`_, `EntropyPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py>`_, `FCMPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py>`_, `CMeansPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py>`_, `HuarngPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py>`_)
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Check out the jupyter notebook on `pyFTS/notebooks/Partitioners.ipynb <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
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Check out the jupyter notebook on `pyFTS/notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
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3. **Data Fuzzyfication**: Each data point of the numerical time series *Y(t)* will be translated to a fuzzy representation (usually one or more fuzzy sets), and then a fuzzy time series *F(t)* is created.
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@ -47,7 +47,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
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Usage examples
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--------------
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There is nothing better than good code examples to start. `Then check out the demo Jupyter Notebooks of the implemented method os pyFTS! <https://github.com/PYFTS/pyFTS/tree/master/pyFTS/notebooks>`_.
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There is nothing better than good code examples to start. `Then check out the demo Jupyter Notebooks of the implemented method os pyFTS! <https://github.com/PYFTS/notebooks>`_.
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A Google Colab example can also be found `here <https://drive.google.com/file/d/1zRBCHXOawwgmzjEoKBgmvBqkIrKxuaz9/view?usp=sharing>`_.
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