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Adaptive Interval Fuzzy Modeling from Stream Data and Application in Cryptocurrencies Forecasting

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Book cover Fuzzy Information Processing 2020 (NAFIPS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1337))

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Abstract

This paper develops an adaptive interval fuzzy modeling method using participatory learning and interval-valued stream data. The model is a collection of fuzzy functional rules whose structure and parameters evolve simultaneously as data are input. The evolving nature of the method allows continuous model update using stream interval data. The method employs participatory learning to cluster interval input data, assigns to each cluster a fuzzy rule, uses the weighted recursive least squares to update the parameters of the rule consequent intervals, and returns an interval-valued output. The efficacy of the method is evaluated in modeling and forecasting daily low and high prices of the two most traded cryptocurrencies, BitCoin and Ethereum. The forecast performance of the adaptive interval fuzzy modeling method is evaluated against classic autorregressive moving average, exponential smoothing state model, and the naïve random walk. Results indicate that, similarly to with exchange rates, no model outperforms random walk in predicting prices in digital coin markets. However, when a measure of directional accuracy is accounted for, adaptive interval fuzzy modeling outperforms the remaining alternatives.

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Notes

  1. 1.

    Selection done choosing cryptocurrencies with the highest liquidity and market capitalization. Data source: https://coinmarketcap.com/.

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Acknowledgements

The authors are grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grants 302467/2019-0 and 304274/2019-4, and the São Paulo Research Foundation (Fapesp) for their support

Author information

Authors and Affiliations

  1. Department of Business, Faculty of Economics, Business and Accounting, University of São Paulo, São Paulo, Brazil

    Leandro Maciel

  2. Department of Economics Theory, Institute of Economics, University of Campinas, Campinas, Brazil

    Rosangela Ballini

  3. Department of Computer Engineering and Automation School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil

    Fernando Gomide

Authors
  1. Leandro Maciel
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  2. Rosangela Ballini
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  3. Fernando Gomide
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Corresponding author

Correspondence to Leandro Maciel .

Editor information

Editors and Affiliations

  1. Department of Mathematics, DigiPen Institute of Technology, Redmond, WA, USA

    Barnabás Bede

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  3. School of Engineering and Technology, University of Washington, Tacoma, WA, USA

    Martine De Cock

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Maciel, L., Ballini, R., Gomide, F. (2022). Adaptive Interval Fuzzy Modeling from Stream Data and Application in Cryptocurrencies Forecasting. In: Bede, B., Ceberio, M., De Cock, M., Kreinovich, V. (eds) Fuzzy Information Processing 2020. NAFIPS 2020. Advances in Intelligent Systems and Computing, vol 1337. Springer, Cham. https://doi.org/10.1007/978-3-030-81561-5_7

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