56 lines
3.8 KiB
Markdown
56 lines
3.8 KiB
Markdown
# pyFTS - Fuzzy Time Series for Python
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## What is pyFTS Library?
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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.
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This project is continously under improvement and contributors are well come.
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## How to reference pyFTS?
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[](https://doi.org/10.5281/zenodo.1194859)
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Silva, P. C. L. et al. *pyFTS: Fuzzy Time Series for Python (Version v4.0).* Belo Horizonte. 2018. DOI: 10.5281/zenodo.1194859. Url: <http://doi.org/10.5281/zenodo.1194859>
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## How to install pyFTS?
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First of all pyFTS was developed and tested with Python 3.6. To install pyFTS using pip tool
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```
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pip install -U pyFTS
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```
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Ou pull directly from the GitHub repo:
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```
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pip install -U git+https://github.com/petroniocandido/pyFTS
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```
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## 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:
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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.
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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:
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- the number of intervals
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- which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/common/Membership.py))
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- 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))
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Check out the jupyter notebook on [pyFTS/notebooks/Partitioners.ipynb](https://github.com/petroniocandido/pyFTS/blob/master/pyFTS/notebooks/Partitioners.ipynb) for sample codes.
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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.
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4. **Generation of Fuzzy Rules**: In this step the temporal transition rules are created. These rules depends on the method and their characteristics:
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- *order*: the number of time lags used on forecasting
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- *weights*: the weighted models introduce weights on fuzzy rules for smoothing
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- *seasonality*: seasonality models depends
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- *steps ahead*: the number of steps ahed to predict. Almost all standard methods are based on one-step-ahead forecasting
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- *forecasting type*: Almost all standard methods are point-based, but pyFTS also provides intervalar and probabilistic forecasting methods.
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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.
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6. **Defuzzyfication**: This step transform the fuzzy forecast into a real number.
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7. **Data postprocessing**: The inverse operations of step 1.
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