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Repository paths correction after migration to PYFTS organization

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@ -28,20 +28,20 @@ pip install -U pyFTS
Ou pull directly from the GitHub repo:
```
pip install -U git+https://github.com/petroniocandido/pyFTS
pip install -U git+https://github.com/PYFTS/pyFTS
```
## What are Fuzzy Time Series (FTS)?
Fuzzy Time Series (FTS) are non parametric methods for time series forecasting based on Fuzzy Theory. The original method was proposed by [1] and improved later by many researchers. The general approach of the FTS methods, based on [2] is listed below:
1. **Data preprocessing**: Data transformation functions contained at [pyFTS.common.Transformations](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/common/Transformations.py), like differentiation, Box-Cox, scaling and normalization.
1. **Data preprocessing**: Data transformation functions contained at [pyFTS.common.Transformations](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Transformations.py), like differentiation, Box-Cox, scaling and normalization.
2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
- the number of intervals
- which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/common/Membership.py))
- partition scheme ([GridPartitioner](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [CMeansPartitioner](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/partitioners/CMeans.py), [HuarngPartitioner](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
- 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)[3], [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)[4])
Check out the jupyter notebook on [pyFTS/notebooks/Partitioners.ipynb](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/notebooks/Partitioners.ipynb) for sample codes.
Check out the jupyter notebook on [pyFTS/notebooks/Partitioners.ipynb](https://github.com/PYFTS/pyFTS/blob/master/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.
@ -60,7 +60,7 @@ 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/petroniocandido/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/pyFTS/tree/master/pyFTS/notebooks).
A Google Colab example can also be found [here](https://drive.google.com/file/d/1zRBCHXOawwgmzjEoKBgmvBqkIrKxuaz9/view?usp=sharing).