Skip to main content
SpringerLink
Log in

An embedding-based non-stationary fuzzy time series method for multiple output high-dimensional multivariate time series forecasting in IoT applications

  • S.I.: Latin American Computational Intelligence
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the internet of things (IoT), high-dimensional time series data are generated continuously and recorded from different data sources; moreover, these time series are characterized by intrinsic changes known as concept drifts. Beside, decision-making in IoT applications may often involve multiple factors and criteria. Therefore, methods capable of handling high-dimensional non-stationary time series and many outputs are of great value in IoT applications. An important gap in the literature is the absence of fuzzy time series (FTS) multiple-input multiple-output (MIMO) methods. To fill this gap, we present a new methodology for forecasting high-dimensional non-stationary time series called MO-ENSFTS (multiple output embedding non-stationary fuzzy time series). MO-ENSFTS is a first-order MIMO multivariate model. We apply a combination of data embedding transformation and a non-stationary FTS model. We tested the proposed methodology on four real-world high-dimensional IoT time-series data sets. The proposed approach is a data-driven method, which is flexible and adaptable for many IoT applications. The computational results show that the proposed method outperforms recurrent neural networks, random forests and support vector regression methods, and is more parsimonious than deep learning methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. https://github.com/hugovynicius/MO-ENSFTS..

References

  1. Nitti M, Pilloni V, Colistra G, Atzori L (2016) The virtual object as a major element of the internet of things: a survey. IEEE Commun Surv Tutor 18(2):1228–1240

    Article  Google Scholar 

  2. Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516

    Article  Google Scholar 

  3. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  4. Ditzler G, Roveri M, Alippi C, Polikar R (2015) Learning in nonstationary environments: a survey. IEEE Compt Int Mag 10(4):12–25

    Article  Google Scholar 

  5. Xu D, Shi Y, Tsang IW, Ong Y-S, Gong C, Shen X (2020) Survey on multi-output learning. IEEE Trans Neural Netw Learn Syst 31(7):2409–2429

    MathSciNet  Google Scholar 

  6. Reyes O, Ventura S (2019) Performing multi-target regression via a parameter sharing-based deep network. Int J Neural Syst 29(09):1950014

    Article  Google Scholar 

  7. Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54(3):269–277

    Article  MathSciNet  MATH  Google Scholar 

  8. Singh P (2015) A brief review of modeling approaches based on fuzzy time series. Int J Mach Learn Cybern 2:397–420

    Google Scholar 

  9. Singh P (2016) Fuzzy time series modeling approaches: a review. Applications of Soft Computing in Time Series Forecasting 11-39

  10. Bose M, Mali K (2019) Designing fuzzy time series forecasting models: a survey. Int J Approx Reason 111:78–99

    Article  MathSciNet  MATH  Google Scholar 

  11. de Lima Silva PC, Severiano Jr CA, Alves MA, Cohen MW, Guimarães FG (2019) A new granular approach for multivariate forecasting. In: Latin American workshop on computational neuroscience, 41–58. Springer

  12. de Lima Silva PC, de Oliveira e Lucas P, Sadaei HJ (2020) Distributed evolutionary hyperparameter optimization for fuzzy time series. IEEE Trans Netw Serv Manag 17(3):1309–1321

    Article  Google Scholar 

  13. de Lima Silva PC, Junior CAS, Alves MA, Silva R, Weiss-Cohen M, Guimarães FG (2020) Forecasting in non-stationary environments with fuzzy time series. Appl Soft Comput 97:106825

    Article  Google Scholar 

  14. Bitencourt HV, Guimarães FG (2021) High-dimensional multivariate time series forecasting in IoT applications using embedding non-stationary fuzzy time series. In: 2021 IEEE Latin American conference on computational intelligence (LA-CCI), 1–6

  15. Manic M, Amarasinghe K, Rodriguez-Andina JJ, Rieger C (2016) Intelligent buildings of the future: cyberaware, deep learning powered, and human interacting. IEEE Ind Electron Mag 10(4):32–49

    Article  Google Scholar 

  16. Mocanu E, Nguyen PH, Gibescu M, Kling WL (2016) Deep learning for estimating building energy consumption. Sustain Energy Grids Netw 6:91–99

    Article  Google Scholar 

  17. Candanedo LM, Feldheim V, Deramaix D (2017) Data driven prediction models of energy use of appliances in a low-energy house. Energy Build 140:81–97

    Article  Google Scholar 

  18. Chammas M, Makhoul A, Demerjian J (2019) An efficient data model for energy prediction using wireless sensors. Comput Electr Eng 76:249–257

    Article  Google Scholar 

  19. Sajjad M, Khan ZA, Ullah A, Hussain T, Ullah W, Lee MY, Baik SW (2020) A novel cnn-gru-based hybrid approach for short-term residential load forecasting. IEEE Access 8:143759–143768

    Article  Google Scholar 

  20. Khan ZA, Ullah A, Ullah W, Rho S, Lee M, Baik SW (2020) Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy. Appl Sci 10(23):8634

    Article  Google Scholar 

  21. Parhizkar T, Rafieipour E, Parhizkar A (2021) Evaluation and improvement of energy consumption prediction models using principal component analysis based feature reduction. J Clean Prod 279:123866

    Article  Google Scholar 

  22. Pearson K (1901) LIII. on lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 11:559–572

    Article  MATH  Google Scholar 

  23. Ameer S, Shah MA, Khan A, Song H, Maple C, Islam SU, Asghar MN (2019) Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE Access 7:128325–128338

    Article  Google Scholar 

  24. Zhang Z, Zeng Y, Yan K (2021) A hybrid deep learning technology for pm 2.5 air quality forecasting. Environ Sci Pollut Res 29:39409–22

    Article  Google Scholar 

  25. Bekkar A, Hssina B, Douzi S, Douzi K (2021) Air-pollution prediction in smart city, deep learning approach. J Big Data 8(1):1–21

    Article  Google Scholar 

  26. Jin N, Zeng Y, Yan K, Ji Z (2021) Multivariate air quality forecasting with nested long short term memory neural network. IEEE Trans Industr Inf 17(12):8514–8522

    Article  Google Scholar 

  27. Munkhdalai L, Munkhdalai T, Park KH, Amarbayasgalan T, Batbaatar E, Park HW, Ryu KH (2019) An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access 7:99099–99114

    Article  Google Scholar 

  28. Garibaldi JM, Ozen T (2007) Uncertain fuzzy reasoning: a case study in modelling expert decision making. IEEE Trans Fuzzy Syst 15(1):16–30

    Article  Google Scholar 

  29. Garibaldi JM, Jaroszewski M, Musikasuwan S (2008) Nonstationary fuzzy sets. IEEE Trans Fuzzy Syst 16(4):1072–1086

    Article  Google Scholar 

  30. Kim KI, Franz MO, Schölkopf B (2005) Iterative kernel principal component analysis for image modeling. IEEE Trans Pattern Anal Mach Intell 27(9):1351–1366

    Article  Google Scholar 

  31. Cheung Y-W, Lai KS (1995) Lag order and critical values of the augmented dickey-fuller test. J Bus Econ Stat 13(3):277–280

    Google Scholar 

  32. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? J Econ 54(1):159–178

    Article  MATH  Google Scholar 

  33. Dua D, Graff C (2017) UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

  34. Kaggle: Smart Home Dataset with weather Information. https://www.kaggle.com/taranvee/smart-home-dataset-with-weather-information. accessed on 28 Ago 2021 (2021)

  35. Zhang S, Guo B, Dong A, He J, Xu Z, Chen SX (2017) Cautionary tales on air-quality improvement in beijing. Proc R Soc A Math Phys Eng Sci 473(2205):20170457

    Google Scholar 

  36. De Vito S, Piga M, Martinotto L, Francia G (2009) Co, no2 and nox urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization. Sens Actuator B Chem 143:182–191

    Article  Google Scholar 

  37. Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621

    Article  MATH  Google Scholar 

  38. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83

    Article  Google Scholar 

  39. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  40. Chollet F et al. (2015) Keras. https://keras.io

  41. Abadi M et al. (2015) TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org. https://www.tensorflow.org/

  42. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch

  43. de Lima Silva PC, et al.: pyFTS: Fuzzy Time Series for Python. https://pyfts.github.io/pyFTS/

  44. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  45. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  46. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  47. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  48. Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V (1996) Support vector regression machines. Adv Neural Inform Process Syst 9

Download references

Acknowledgements

This work has been supported by the Brazilian agencies (1) National Council for Scientific and Technological Development (CNPq), Grant no. 312991/2020-7; (2) Coordination for the Improvement of Higher Education Personnel (CAPES) and (3) Foundation for Research of the State of Minas Gerais (FAPEMIG, in Portuguese). MINDS Laboratory – https://minds.eng.ufmg.br/

Author information

Authors and Affiliations

  1. Machine Intelligence and Data Science Lab (MINDS), Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, 31270-901, Brazil

    Hugo Vinicius Bitencourt, Omid Orang, Luiz Augusto Facury de Souza & Frederico Gadelha Guimarães

  2. Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, 31270-901, Brazil

    Hugo Vinicius Bitencourt & Omid Orang

  3. Instituto Federal do Norte de Minas Gerais (IFNMG), Januaria, MG, 39480-000, Brazil

    Petrônio C. L. Silva

Authors
  1. Hugo Vinicius Bitencourt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omid Orang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luiz Augusto Facury de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Petrônio C. L. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Frederico Gadelha Guimarães
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frederico Gadelha Guimarães.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bitencourt, H.V., Orang, O., de Souza, L.A.F. et al. An embedding-based non-stationary fuzzy time series method for multiple output high-dimensional multivariate time series forecasting in IoT applications. Neural Comput & Applic 35, 9407–9420 (2023). https://doi.org/10.1007/s00521-022-08120-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08120-5

Keywords

Search

Navigation