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Highly Stochastic Time Series Modeling using HTM in Comparison with Commonly Used Methods

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Proceedings of Seventh International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 448))

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Abstract

This study compares the HTM models applicability in highly stochastic time series forecasting problems, to a range of commonly used approaches. The models were tested on a real-world data, representing raw material usage in a food processing company. The comparison was done on a set of 21 data series with a high disparity of underlying process characteristics. HTM models were evaluated against 6 other approaches. As a result, HTM models were able to outperform other models in 8 out of 21 cases, with an average improvement of around 20% of RMSE value, scoring in the first place as a most accurate approach.

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Authors and Affiliations

  1. Smart Geometries Sp. z o.o., Lęborska 8/10/183, Warszawa, Poland

    Filip Begiełło & Tomasz Bławucki

Authors
  1. Filip Begiełło
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  2. Tomasz Bławucki
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Correspondence to Filip Begiełło .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. The University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Begiełło, F., Bławucki, T. (2023). Highly Stochastic Time Series Modeling using HTM in Comparison with Commonly Used Methods. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-19-1610-6_10

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  • DOI: https://doi.org/10.1007/978-981-19-1610-6_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1609-0

  • Online ISBN: 978-981-19-1610-6

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