Learning Novel Transformer Architecture for Time-series Forecasting

Juyuan Zhang; Wei Zhu; Jiechao Gao

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arXiv · 2502.13721

Learning Novel Transformer Architecture for Time-series Forecasting

Juyuan Zhang·Wei Zhu·Jiechao Gao

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Abstract

Despite the success of Transformer-based models in the time-series prediction (TSP) tasks, the existing Transformer architecture still face limitations and the literature lacks comprehensive explorations into alternative architectures. To address these challenges, we propose AutoFormer-TS, a novel framework that leverages a comprehensive search space for Transformer architectures tailored to TSP tasks. Our framework introduces a differentiable neural architecture search (DNAS) method, AB-DARTS, which improves upon existing DNAS approaches by enhancing the identification of optimal operations within the architecture. AutoFormer-TS systematically explores alternative attention mechanisms, activation functions, and encoding operations, moving beyond the traditional Transformer design. Extensive experiments demonstrate that AutoFormer-TS consistently outperforms state-of-the-art baselines across various TSP benchmarks, achieving superior forecasting accuracy while maintaining reasonable training efficiency.

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Juyuan Zhang, Wei Zhu, Jiechao Gao. 2025-02-19. Learning Novel Transformer Architecture for Time-series Forecasting. https://arxiv.org/abs/2502.13721

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