diff --git a/README.md b/README.md index 2e9fe48..3e19061 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,35 @@ -# pyFTSpackage -pyFTS repository for pyPI package +# pyFTS - Fuzzy Time Series for Python + +## 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, 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) + - partition scheme (GridPartitioner, EntropyPartitioner, FCMPartitioner, CMeansPartitioner, HuarngPartitioner) + + Check the jupyter notebook /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**: + +6. **Defuzzyfication** + +7. **Data postprocessing**: The inverse operations of step 1. +