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

2025-07-28

AI Summary

Existing time-series forecasting models struggle to effectively capture spatiotemporal dependencies and integrate multi-scale global contextual information. To address this, we propose a novel forecasting framework comprising three key components: (1) an adaptive fuzzy time-series construction method to enhance temporal robustness; (2) a partially asymmetric convolutional architecture enabling automatic fine-grained feature extraction within sliding windows; and (3) a bilateral atrous algorithm combined with a sub-sliding-window mechanism and multi-scale feature fusion strategy to jointly model local and global context while reducing computational complexity. Extensive experiments on multiple mainstream benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods in forecasting accuracy, inference efficiency, and representational capacity, validating its comprehensive advantages in modeling complex temporal dynamics.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Improve fuzzy time series to capture spatio-temporal dependency
• Reduce calculation demand without losing global characteristics
• Design asymmetric convolution for flexible multi-scale feature mining

Innovation

• Adaptive fuzzified construction for temporal data
• Bilateral Atrous algorithm reduces calculation demand
• Partially asymmetric CNN mines multi-scale features