# Adaptive Fuzzy Time Series Forecasting via Partially Asymmetric Convolution and Sub-Sliding Window Fusion

arXiv: https://arxiv.org/abs/2507.20641 · HTML: https://mlsanity.com/paper/2507.20641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 90 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://mlsanity.com/paper/1908.03930/) (2019)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Revisit Self-Debugging with Self-Generated Tests for Code Generation](https://mlsanity.com/paper/2501.12793/) (2025)
- [Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction](https://mlsanity.com/paper/2106.04751/) (2021)
- [Time Series Forecasting via Learning Convolutionally Low-Rank Models](https://mlsanity.com/paper/2104.11510/) (2021)
- [Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments](https://mlsanity.com/paper/2006.10119/) (2020)
- [Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action Localization](https://mlsanity.com/paper/2412.19418/) (2024)
- [MMGET: A Markov model for generalized evidence theory](https://mlsanity.com/paper/2105.07952/) (2021)
- [A Matrix-based Distance of Pythagorean Fuzzy Set and its Application in Medical Diagnosis](https://mlsanity.com/paper/2102.01538/) (2021)
- [Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.06177/) (2024)
- [Rethinking Regularization Methods for Knowledge Graph Completion](https://mlsanity.com/paper/2505.23442/) (2025)
- [Differential Convolutional Fuzzy Time Series Forecasting](https://mlsanity.com/paper/2305.08890/) (2023)
- [An Adaptive Framework for Multi-View Clustering Leveraging Conditional Entropy Optimization](https://mlsanity.com/paper/2412.17647/) (2024)

## Abstract (arXiv)

At present, state-of-the-art forecasting models are short of the ability to capture spatio-temporal dependency and synthesize global information at the stage of learning. To address this issue, in this paper, through the adaptive fuzzified construction of temporal data, we propose a novel convolutional architecture with partially asymmetric design based on the scheme of sliding window to realize accurate time series forecasting. First, the construction strategy of traditional fuzzy time series is improved to further extract short and long term temporal interrelation, which enables every time node to automatically possess corresponding global information and inner relationships among them in a restricted sliding window and the process does not require human involvement. Second, a bilateral Atrous algorithm is devised to reduce calculation demand of the proposed model without sacrificing global characteristics of elements. And it also allows the model to avoid processing redundant information. Third, after the transformation of time series, a partially asymmetric convolutional architecture is designed to more flexibly mine data features by filters in different directions on feature maps, which gives the convolutional neural network (CNN) the ability to construct sub-windows within existing sliding windows to model at a more fine-grained level. And after obtaining the time series information at different levels, the multi-scale features from different sub-windows will be sent to the corresponding network layer for time series information fusion. Compared with other competitive modern models, the proposed method achieves state-of-the-art results on most of popular time series datasets, which is fully verified by the experimental results.