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Updating the links due to notebook refactoring

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Petrônio Cândido de Lima e Silva 2018-11-01 11:31:28 -03:00 committed by GitHub
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docs/quickstart.rst
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@ -27,7 +27,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
- which fuzzy membership function (on `pyFTS.common.Membership <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py>`_)
- 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>`_)
Check out the jupyter notebook on `pyFTS/notebooks/Partitioners.ipynb <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
Check out the jupyter notebook on `pyFTS/notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
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.
@ -47,7 +47,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
Usage examples
--------------
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>`_.
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>`_.
A Google Colab example can also be found `here <https://drive.google.com/file/d/1zRBCHXOawwgmzjEoKBgmvBqkIrKxuaz9/view?usp=sharing>`_.