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A Distributed Algorithm for Scalable Fuzzy Time Series

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Book cover Green, Pervasive, and Cloud Computing (GPC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11484))

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Abstract

Time series forecasting, an essential task in management of Smart Cities and Smart Grids, becomes challenging when it needs to deal with big data. The development of highly accurate machine learning models is yet harder when considering the optimization of hyper-parameters, an expensive computational task. To tame these challenges this work proposes the Weighted Multivariate Fuzzy Time Series method, a simple and non-parametric forecasting method with high scalability and accuracy. A stack of methods is presented, which comprises a sequential training and forecasting procedure and a Map/Reduce extension for distributed processing. The stack of proposed methods was evaluated using a cluster with commodity hardware and a big data of solar energy time series, achieving good performance in feasible processing time.

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Notes

  1. 1.

    https://hadoop.apache.org/.

  2. 2.

    http://sonda.ccst.inpe.br/.

  3. 3.

    Code: BRB. Coordinates: 15°36’ 03” S 47°42’47” O. Alt.: 1023m.

  4. 4.

    https://spark.apache.org/.

References

  1. Alves, M.A., Silva, P.C.D.L., Severiano, C.A.J., Vieira, G.L., Guimaraes, F.G., Sadaei, H.J.: An extension of nonstationary fuzzy sets to heteroskedastic fuzzy time series. In: 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium (2018)

    Google Scholar 

  2. Askari, S., Montazerin, N.: A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering. Expert Syst. Appl. 42(4), 2121–2135 (2015). https://doi.org/10.1016/j.eswa.2014.09.036

    Article  Google Scholar 

  3. Cai, Q., Zhang, D., Zheng, W., Leung, S.C.H.: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression (2015). https://doi.org/10.1016/j.knosys.2014.11.003

  4. Carvalho Jr., J., Costa Jr., C.: Identification method for fuzzy forecasting models of time series. Appl. Soft Comput. 50, 166–182 (2017). https://doi.org/10.1016/j.asoc.2016.11.003

    Article  Google Scholar 

  5. Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996). https://doi.org/10.1016/0165-0114(95)00220-0

  6. Chen, S.M., Phuong, B.D.H.: Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowl.-Based Syst. (2017). https://doi.org/10.1016/j.knosys.2016.11.019

    Article  Google Scholar 

  7. Chen, S.M., Tanuwijaya, K.: Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques. Expert Syst. Appl. 38 (2011). https://doi.org/10.1016/j.eswa.2011.02.098

  8. Cheng, C.H., Chen, T.L., Teoh, H.J., Chiang, C.H.: Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Syst. Appl. 34, 1126–1132 (2008). https://doi.org/10.1016/j.eswa.2006.12.021

    Article  Google Scholar 

  9. Cheng, C.H.H., Chang, J.R.R., Yeh, C.A.A.: Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost. Technol. Forecasting Soc. Change 73(5), 524–542 (2006). https://doi.org/10.1016/j.techfore.2005.07.004

  10. Coelho, V.N., et al.: A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment. Appl. Energy 169, 567–584 (2016). https://doi.org/10.1016/j.apenergy.2016.02.045

    Article  Google Scholar 

  11. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  12. Dincer, N.G., Akkuş, Ã.: A new fuzzy time series model based on robust clustering for forecasting of air pollution. Ecol. Inform. 43, 157–164 (2018). https://doi.org/10.1016/J.ECOINF.2017.12.001

  13. Guney, H., Bakir, M.A., Aladag, C.H.: A novel stochastic seasonal fuzzy time series forecasting model. Int. J. Fuzzy Syst. (2018). https://doi.org/10.1007/s40815-017-0385-z

  14. Huarng, K.: Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 123(3), 387–394 (2001). https://doi.org/10.1016/S0165-0114(00)00057-9

    Article  MathSciNet  MATH  Google Scholar 

  15. Pal, S.S., Kar, S.: Fuzzy time series model for unequal interval length using genetic algorithm. In: Chandra, P., Giri, D., Li, F., Kar, S., Jana, D.K. (eds.) Information Technology and Applied Mathematics. AISC, vol. 699, pp. 205–216. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-7590-2_15

    Chapter  Google Scholar 

  16. Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S.: A survey of machine learning for big data processing. EURASIP J. Adv. Sig. Process. 67 (2016). https://doi.org/10.1186/s13634-016-0355-x

  17. Sadaei, H.J., Enayatifar, R., Abdullah, A.H., Gani, A.: Short-term 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. 62(from 2005), 118–129 (2014). https://doi.org/10.1016/j.ijepes.2014.04.026

  18. Sadaei, H.J., Enayatifar, R., Lee, M.H., Mahmud, M.: A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting. Appl. Soft Comput. J. 40, 132–149 (2016). https://doi.org/10.1016/j.asoc.2015.11.026

    Article  Google Scholar 

  19. Sadaei, H.J., Guimarães, F.G., Silva, C.J.D., Lee, M.H., Eslami, T.: Short-term load forecasting method based on fuzzy time series, seasonality and long memory process. Int. J. Approximate Reason. 83, 196–217 (2017). https://doi.org/10.1016/j.ijar.2017.01.006

  20. Severiano, C.A.C., Silva, P.P.C.L., Sadaei, H.J.H., Guimarães, F.F.G.: Very short-term solar forecasting using fuzzy time series. In: 2017 IEEE International Conference on Fuzzy Systems, Naples, Italy (2017). https://doi.org/10.1109/FUZZ-IEEE.2017.8015732

  21. Silva, G.C., et al.: Fuzzy time series applications and extensions: analysis of a short term load forecasting challenge. In: International Conference on Time Series and Forecasting (ITISE 2018), vol. 770, Granada, Spain (2018). https://www.researchgate.net/publication/327756074

  22. Silva, G.C., Silva, J.L.R., Lisboa, A.C., Vieira, D.A.G., Saldanha, R.R.: Advanced fuzzy time series applied to short term load forecasting. In: 4th IEEE Latin American Conference on Computational Intelligence. IEE, Guadalajara (2017)

    Google Scholar 

  23. Silva, P., Sadaei, H.J., Guimarães, F.: Interval forecasting with fuzzy time series. In: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athens, Greece (2016). https://doi.org/10.1109/SSCI.2016.7850010

  24. de Lima e Silva, P.C.: pyFTS: Fuzzy Time Series for Python (2018). https://doi.org/10.5281/zenodo.597359

  25. de Lima e Silva, P.C., Severiano Jr., C.A., Vieira, G.L., Guimarães, F.G., Sadaei, H.J.: Probabilistic forecasting with seasonal ensemble fuzzy time-series. In: XIII Brazilian Congress on Computational Intelligence, Rio de Janeiro (2017)

    Google Scholar 

  26. Singh, P.: Big Data Time Series Forecasting Model: A Fuzzy-Neuro Hybridize Approach (2015). https://doi.org/10.1007/978-3-319-16598-1_2

  27. Singh, P.: A brief review of modeling approaches based on fuzzy time series. J. Mach. Learn. Cybern. 8(2), 397–420 (2017).https://doi.org/10.1007/s13042-015-0332-y

  28. Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54(3), 269–277 (1993). https://doi.org/10.1016/0165-0114(93)90372-O

    Article  MathSciNet  MATH  Google Scholar 

  29. Talarposhti, F.M., Sadaei, H.J., Enayatifar, R., Guimarães, F.G., Mahmud, M., Eslami, T.: Stock market forecasting by using a hybrid model of exponential fuzzy time series. Int. J. Approximate Reason. 70, 79–98 (2016). https://doi.org/10.1016/j.ijar.2015.12.011

    Article  MathSciNet  MATH  Google Scholar 

  30. White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Inc., Sebastopol (2012)

    Google Scholar 

  31. Yu, H.K.: Weighted fuzzy time series models for TAIEX forecasting. Phys. A: Stat. Mech. Appl. 349(3), 609–624 (2005). https://doi.org/10.1016/j.physa.2004.11.006

    Article  Google Scholar 

  32. Zhang, S., et al.: Two-factor high-order fuzzy-trend FTS model based on BSO-FCM and improved KA for TAIEX stock forecastin. Nonlinear Dyn. (2018). https://doi.org/10.1007/s11071-018-4433-5

  33. Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237 (2017). https://doi.org/10.1016/j.neucom.2017.01.026

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Author information

Authors and Affiliations

  1. Instituto Federal do Norte de Minas Gerais - Campus Januária, Januária, Brazil

    Petrônio Cândido de Lima e Silva

  2. Instituto Federal do Norte de Minas Gerais - Campus Salinas, Salinas, Brazil

    Patrícia de Oliveira e Lucas

  3. Machine Intelligence and Data Science Lab (MINDS UFMG), Belo Horizonte, Brazil

    Petrônio Cândido de Lima e Silva, Patrícia de Oliveira e Lucas & Frederico Gadelha Guimarães

  4. Department of Electrical Engineering, Universidade Federal de Minas Gerais, UFMG, Belo Horizonte, Brazil

    Frederico Gadelha Guimarães

Authors
  1. Petrônio Cândido de Lima e Silva
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  2. Patrícia de Oliveira e Lucas
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  3. Frederico Gadelha Guimarães
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Corresponding author

Correspondence to Petrônio Cândido de Lima e Silva .

Editor information

Editors and Affiliations

  1. Federal University of Uberlândia, Uberlândia, Brazil

    Rodrigo Miani

  2. Federal University of Uberlândia, Uberlândia, Brazil

    Lasaro Camargos

  3. State University of Londrina, Londrina, Brazil

    Bruno Zarpelão

  4. Federico Santa María Technical University, San Joaquin, Chile

    Erika Rosas

  5. Federal University of Uberlândia, Uberlândia, Brazil

    Rafael Pasquini

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de Lima e Silva, P.C., de Oliveira e Lucas, P., Guimarães, F.G. (2019). A Distributed Algorithm for Scalable Fuzzy Time Series. In: Miani, R., Camargos, L., Zarpelão, B., Rosas, E., Pasquini, R. (eds) Green, Pervasive, and Cloud Computing. GPC 2019. Lecture Notes in Computer Science(), vol 11484. Springer, Cham. https://doi.org/10.1007/978-3-030-19223-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-19223-5_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19222-8

  • Online ISBN: 978-3-030-19223-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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