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

TL;DR

This paper introduces an innovative adaptive fuzzy convolutional neural network with a partially asymmetric design and sub-sliding window fusion, significantly improving time series forecasting by capturing complex spatio-temporal dependencies.

Contribution

It proposes a novel fuzzy time series construction, bilateral Atrous algorithm, and a partially asymmetric convolutional architecture for enhanced feature extraction and global information synthesis.

Findings

Achieves state-of-the-art forecasting accuracy on multiple datasets.

Effectively captures short and long-term temporal dependencies.

Reduces computational complexity while maintaining global features.

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.