A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction Article Swipe
Fulong Yao
,
Wanqing Zhao
,
Matthew Forshaw
,
Yang Song
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.08010
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.08010
Data uncertainty is inherent in many real-world applications and poses significant challenges for accurate time series predictions. The interval type 2 fuzzy neural network (IT2FNN) has shown exceptional performance in uncertainty modelling for single-step prediction tasks. However, extending it for multi-step ahead predictions introduces further issues in uncertainty handling as well as model interpretability and accuracy. To address these issues, this paper proposes a new selforganizing interval type-2 fuzzy neural network with multiple outputs (SOIT2FNN-MO). Differing from the traditional six-layer IT2FNN, a nine-layer network architecture is developed. First, a new co-antecedent layer and a modified consequent layer are devised to improve the interpretability of the fuzzy model for multi-step time series prediction problems. Second, a new link layer is created to improve the accuracy by building temporal connections between multi-step predictions. Third, a new transformation layer is designed to address the problem of the vanishing rule strength caused by high-dimensional inputs. Furthermore, a two-stage, self-organizing learning mechanism is developed to automatically extract fuzzy rules from data and optimize network parameters. Experimental results on chaotic and microgrid prediction problems demonstrate that SOIT2FNN-MO outperforms state-of-the-art methods, by achieving a better accuracy ranging from 1.6% to 30% depending on the level of noises in data. Additionally, the proposed model is more interpretable, offering deeper insights into the prediction process.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.08010
- https://arxiv.org/pdf/2407.08010
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400611354
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- OpenAlex ID
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https://openalex.org/W4400611354Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.08010Digital Object Identifier
- Title
-
A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-10Full publication date if available
- Authors
-
Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang SongList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.08010Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.08010Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.08010Direct OA link when available
- Concepts
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Artificial neural network, Series (stratigraphy), Interval (graph theory), Computer science, Time series, Type (biology), Artificial intelligence, Fuzzy logic, Mathematics, Machine learning, Paleontology, Biology, Combinatorics, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.predictions | 42 |
| abstract_inverted_index.significant | 10 |
| abstract_inverted_index.single-step | 33 |
| abstract_inverted_index.traditional | 78 |
| abstract_inverted_index.uncertainty | 1, 30, 47 |
| abstract_inverted_index.Experimental | 170 |
| abstract_inverted_index.Furthermore, | 151 |
| abstract_inverted_index.applications | 7 |
| abstract_inverted_index.architecture | 84 |
| abstract_inverted_index.predictions. | 16, 130 |
| abstract_inverted_index.Additionally, | 202 |
| abstract_inverted_index.automatically | 160 |
| abstract_inverted_index.co-antecedent | 90 |
| abstract_inverted_index.(SOIT2FNN-MO). | 74 |
| abstract_inverted_index.interpretable, | 208 |
| abstract_inverted_index.selforganizing | 65 |
| abstract_inverted_index.transformation | 134 |
| abstract_inverted_index.self-organizing | 154 |
| abstract_inverted_index.high-dimensional | 149 |
| abstract_inverted_index.interpretability | 53, 102 |
| abstract_inverted_index.state-of-the-art | 182 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |