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)
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.
