pyFTS  Fuzzy Time Series for Python
What is 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 humanreadable models, suitable for statistic laymans to experts.
This project is continously under improvement and contributors are well come.
How to reference pyFTS?
Silva, P. C. L. et al. pyFTS: Fuzzy Time Series for Python. Belo Horizonte. 2018. DOI: 10.5281/zenodo.597359. Url: http://doi.org/10.5281/zenodo.597359
How to install pyFTS?
First of all pyFTS was developed and tested with Python 3.6. To install pyFTS using pip tool
pip install U pyFTS
Ou pull directly from the GitHub repo:
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:

Data preprocessing: Data transformation functions contained at pyFTS.common.Transformations, like differentiation, BoxCox, scaling and normalization.

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:
Check out the jupyter notebook on notebooks/Partitioners.ipynb for sample codes.

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.

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 [5],[6],[7]

seasonality: seasonality models depends [8]

steps ahead: the number of steps ahed to predict. Almost all standard methods are based on onestepahead forecasting

forecasting type: Almost all standard methods are pointbased, but pyFTS also provides intervalar and probabilistic forecasting methods.

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.

Defuzzyfication: This step transform the fuzzy forecast into a real number.

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.
MINDS  Machine Intelligence And Data Science Lab
This tool is result of collective effort of MINDS Lab, headed by Prof. Frederico Gadelha Guimaraes. Some of research on FTS which was developed under pyFTS:

2020
 ORANG, Omid; Solar Energy Forecasting With Fuzzy Time Series Using HighOrder Fuzzy Cognitive Maps. IEEE World Congress On Computational Intelligence 2020 (WCCI).
 ALYOUSIFI, Y; FAYE, Othman M; SOKKALINGAM, I; SILVA, P. Markov Weighted Fuzzy TimeSeries Model Based on an Optimum Partition Method for Forecasting Air Pollution. International Journal of Fuzzy Systems, 2020. http://doi.org/10.1007/s4081502000841w
 SILVA, Petrônio CL et al. Forecasting in Nonstationary Environments with Fuzzy Time Series. https://arxiv.org/abs/2004.12554
 SILVA, Petrônio CL et al. Distributed Evolutionary Hyperparameter Optimization for Fuzzy Time Series. IEEE Transactions on Network and Service Management, 2020. http://doi.org/10.1109/TNSM.2020.2980289
 ALYOUSIFI, Yousif et al. Predicting Daily Air Pollution Index Based on Fuzzy Time Series Markov Chain Model. Symmetry, v. 12, n. 2, p. 293, 2020. http://doi.org/10.3390/sym12020293

2019
 SILVA, Petrônio C. L. Scalable Models of Fuzzy Time Series for Probabilistic Forecasting. PhD Thesis. https://doi.org/10.5281/zenodo.3374641
 SADAEI, Hossein J. et al. Shortterm load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, v. 175, p. 365377, 2019. http://doi.org/10.1016/j.energy.2019.03.081
 SILVA, Petrônio CL et al. Probabilistic forecasting with fuzzy time series. IEEE Transactions on Fuzzy Systems, 2019. http://doi.org/10.1109/TFUZZ.2019.2922152
 SILVA, Petrônio C. L.; LUCAS, Patrícia de O.; GUIMARÃES, Frederico Gadelha. A Distributed Algorithm for Scalable Fuzzy Time Series. In: International Conference on Green, Pervasive, and Cloud Computing. Springer, Cham, 2019. p. 4256. http://doi.org/10.1007/9783030192235_4
 SILVA, Petrônio Cândido de Lima et al. A New Granular Approach for Multivariate Forecasting. In: Latin American Workshop on Computational Neuroscience. Springer, Cham, 2019. p. 4158. http://doi.org/10.1007/9783030366360_4
 ALVES, Marcos Antonio et al. Otimizaçao Dinâmica Evolucionária para Despacho de Energia em uma Microrrede usando Veıculos Elétricos. Em: Anais do 14º Simpósio Brasileiro de Automação Inteligente. Campinas : GALOÁ. 2019. http://doi.org/10.17648/sbai2019111524
 LUCAS, Patrícia de O.; SILVA, Petrônio C. L.; GUIMARAES, Frederico G. Otimização Evolutiva de Hiperparâmetros para Modelos de Séries Temporais Nebulosas.Em: Anais do 14º Simpósio Brasileiro de Automação Inteligente. Campinas : GALOÁ. 2019. http://doi.org/10.17648/sbai2019111141

2018
 ALVES, Marcos Antônio et al. An extension of nonstationary fuzzy sets to heteroskedastic fuzzy time series. In: ESANN. 2018.

2017
 SEVERIANO, Carlos A. et al. Very shortterm solar forecasting using fuzzy time series. In: 2017 IEEE international conference on fuzzy systems (FUZZIEEE). IEEE, 2017. p. 16. http://doi.org/10.1109/FUZZIEEE.2017.8015732
 SILVA, Petrônio C. L.; et al. Probabilistic forecasting with seasonal ensemble fuzzy timeseries. In: XIII Brazilian Congress on Computational Intelligence, Rio de Janeiro. 2017. http://doi.org/10.21528/CBIC201754
 COSTA, Francirley R. B.; SILVA, Petrônio C. L.; GUIMARAES, Frederico G. REGRESSÃO LINEAR APLICADA NA PREDIÇÃO DE SERIES TEMPORAIS FUZZY. Simpósio Brasileiro de Automação Inteligente (SBAI), 2017.

2016
 SILVA, Petrônio C. L.; SADAEI, Hossein Javedani; GUIMARAES, Frederico G. Interval forecasting with fuzzy time series. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. p. 18. http://doi.org/10.1109/SSCI.2016.7850010
References
 Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.
 S.M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
 C. H. Cheng, R. J. Chang, and C. A. Yeh, “Entropybased and trapezoidal fuzzificationbased fuzzy time series approach for forecasting IT project cost”. Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524–542, Jun. 2006.
 K. H. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series”. Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, Nov. 2001.
 H.K. Yu, “Weighted fuzzy time series models for TAIEX forecasting”. Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.
 R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.
 H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Shortterm load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.
 C.H. Cheng, Y.S. Chen, and Y.L. Wu, “Forecasting innovation diffusion of products using trendweighted fuzzy timeseries model,” Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.