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< li > < a class = "reference internal" href = "#" > pyFTS - Fuzzy Time Series for Python< / a > < ul >
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< li > < a class = "reference internal" href = "#what-is-pyfts-library" > What is pyFTS Library?< / a > < / li >
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< h1 > pyFTS - Fuzzy Time Series for Python< a class = "headerlink" href = "#pyfts-fuzzy-time-series-for-python" title = "Permalink to this headline" > ¶< / a > < / h1 >
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< h2 > What is pyFTS Library?< a class = "headerlink" href = "#what-is-pyfts-library" title = "Permalink to this headline" > ¶< / a > < / h2 >
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< span id = "forkongithub" > < a href = "https://github.com/PYFTS/pyFTS" > Fork me on GitHub< / a > < / span > < p > 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 from statistic laymans to experts.< / p >
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< p > This tool is developed on < a class = "reference external" href = "http://www.minds.eng.ufmg.br/" > MINDS Lab< / a > ,
headed by Prof. Frederico Gadelha Guimarães from < a class = "reference external" href = "https://www.ppgee.ufmg.br/" > Electrical
Engineering Departament< / a > of < a class = "reference external" href = "https://ufmg.br/" > Federal University
of Minas Gerais (UFMG)< / a > at Brazil. Also colaborate
with this tool the Brazilian instituitions < a class = "reference external" href = "http://www.ifnmg.edu.br" > Federal Institute of
North of Minas Gerais (IFNMG)< / a > and < a class = "reference external" href = "http://www.ifmg.edu.br" > Federal Institute of Minas Gerais
(IFMG)< / a > .< / p >
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< li class = "toctree-l2" > < a class = "reference internal" href = "quickstart.html#how-to-install-pyfts" > How to install pyFTS?< / a > < / li >
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< a class = "reference external image-reference" href = "https://doi.org/10.5281/zenodo.597359" > < img alt = "https://zenodo.org/badge/DOI/10.5281/zenodo.597359.svg" src = "https://zenodo.org/badge/DOI/10.5281/zenodo.597359.svg" / > < / a >
< p > Silva, P. C. L. et al. < em > pyFTS: Fuzzy Time Series for Python.< / em > Belo Horizonte. 2018. DOI: 10.5281/zenodo.597359. Url: < < a class = "reference external" href = "https://doi.org/10.5281/zenodo.597359" > https://doi.org/10.5281/zenodo.597359< / a > > < / p >
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