Before of all, pyFTS was developed and tested with Python 3.6. To install pyFTS using pip tool
pip install -U pyFTS
Ou clone directly from the GitHub repo for the most recent review:
pip install -U git+https://github.com/PYFTS/pyFTS
What are Fuzzy Time Series (FTS)?
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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/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/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py>`_)
Check out the jupyter notebook on `notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/blob/master/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.
4.**Generation of Fuzzy Rules**: In this step the temporal transition rules are created. These rules depends on the method and their characteristics:
-*order*: the number of time lags used on forecasting
-*weights*: the weighted models introduce weights on fuzzy rules for smoothing
-*seasonality*: seasonality models
-*steps ahead*: the number of steps ahed to predict. Almost all standard methods are based on one-step-ahead forecasting
-*forecasting type*: Almost all standard methods are point-based, but pyFTS also provides intervalar and probabilistic forecasting methods.
5.**Forecasting**: The forecasting step takes a sample (with minimum length equal to the model's order) and generate a fuzzy outputs (fuzzy set(s)) for the next time ahead.
6.**Defuzzyfication**: This step transform the fuzzy forecast into a real number.
7.**Data postprocessing**: The inverse operations of step 1.
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>`_.
Part I: `Introduction to the Fuzzy Logic, Fuzzy Time Series and the pyFTS library <https://towardsdatascience.com/a-short-tutorial-on-fuzzy-time-series-dcc6d4eb1b15>`_.
Part II: `High order, weighted and multivariate methods and a case study of solar energy forecasting. <https://towardsdatascience.com/a-short-tutorial-on-fuzzy-time-series-part-ii-with-an-case-study-on-solar-energy-bda362ecca6d>`_.
Part III: `Interval and probabilistic forecasting, non-stationary time series, concept drifts and time variant models. <https://towardsdatascience.com/a-short-tutorial-on-fuzzy-time-series-part-iii-69445dff83fb>`_.
