pyFTS Quick Start ================= How to install pyFTS? --------------------- .. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg 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)? --------------------------------- 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 `_, `HuarngPartitioner `_) Check out the jupyter notebook on `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 - *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. 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! `_. A Google Colab example can also be found `here `_. A short tutorial on Fuzzy Time Series ------------------------------------- Part I: `Introduction to the Fuzzy Logic, Fuzzy Time Series and the pyFTS library `_. Part II: `High order, weighted and multivariate methods and a case study of solar energy forecasting. `_. Part III: `Interval and probabilistic forecasting, non-stationary time series, concept drifts and time variant models. `_.