# A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction

arXiv: https://arxiv.org/abs/2407.08010 · HTML: https://mlsanity.com/paper/2407.08010/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 70 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Evaluation of deep learning models for multi-step ahead time series prediction](https://mlsanity.com/paper/2103.14250/) (2021)
- [Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression](https://mlsanity.com/paper/1401.2504/) (2014)
- [IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation](https://mlsanity.com/paper/2108.08704/) (2021)
- [A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2403.01013/) (2024)
- [An Online RFID Localization in the Manufacturing Shopfloor](https://mlsanity.com/paper/1805.07715/) (2018)
- [A Non-Intrusive Load Monitoring Approach for Very Short Term Power Predictions in Commercial Buildings](https://mlsanity.com/paper/2007.11819/) (2020)

## Built on by (most cited first, 3 of 3)

- [KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning](https://mlsanity.com/paper/2602.03034/) (2026)
- [Adaptive Fuzzy Time Series Forecasting via Partially Asymmetric Convolution and Sub-Sliding Window Fusion](https://mlsanity.com/paper/2507.20641/) (2025)
- [Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting](https://mlsanity.com/paper/2511.09789/) (2025)

## Abstract (arXiv)

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.