Merge branch 'master' of https://github.com/petroniocandido/pyFTS

README.md

@@ -1,2 +1,36 @@
-# pyFTSpackage
+# pyFTS - Fuzzy Time Series for Python
-pyFTS repository for pyPI package
+
+## pyFTS Library
+
+This package is intended for students, researchers, data scientists or whose want to exploit the Fuzzy Time Series methods. These methods provide simple, easy to use, computationally cheap and human-readable models, suitable for statistic laymans to experts.
+
+This project is continously under improvement and contributors are well come.
+
+
+## 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.
+
+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), [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))
+ 
+ Check out the jupyter notebook on [pyFTS/notebooks/Partitioners.ipynb](https://github.com/petroniocandido/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.
+
+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 depends 
+- *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.
+