Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, distinguished by its focus on extensive input sequences, in contrast to the constrained lengths typical of traditional approaches. While longer sequences inherently convey richer information, potentially enhancing predictive precision, prevailing techniques often respond by escalating model complexity. These intricate models can inflate into millions of parameters, incorporating parameter-intensive elements like positional encodings, feed-forward networks and self-attention mechanisms. This complexity, however, leads to prohibitive model scale, particularly given the time series data's semantic simplicity. Motivated by the pursuit of parsimony, our research employs conditional correlation and auto-correlation as investigative tools, revealing significant redundancies within the input data. Leveraging these insights, we introduce the HDformer, a lightweight Transformer variant enhanced with hierarchical decomposition. This novel architecture not only inverts the prevailing trend toward model expansion but also accomplishes precise forecasting with drastically fewer computations and parameters. Remarkably, HDformer outperforms existing state-of-the-art LTSF models, while requiring over 99\% fewer parameters. Through this work, we advocate a paradigm shift in LTSF, emphasizing the importance to tailor the model to the inherent dynamics of time series data-a timely reminder that in the realm of LTSF, bigger is not invariably better.

Human pose estimation is a challenging task due to its structured data sequence nature. Existing methods primarily focus on pair-wise interaction of body joints, which is insufficient for scenarios involving overlapping joints and rapidly changing poses. To overcome these issues, we introduce a novel approach, the High-order Directed Transformer (HDFormer), which leverages high-order bone and joint relationships for improved pose estimation. Specifically, HDFormer incorporates both self-attention and high-order attention to formulate a multi-order attention module. This module facilitates first-order "joint$\leftrightarrow$joint", second-order "bone$\leftrightarrow$joint", and high-order "hyperbone$\leftrightarrow$joint" interactions, effectively addressing issues in complex and occlusion-heavy situations. In addition, modern CNN techniques are integrated into the transformer-based architecture, balancing the trade-off between performance and efficiency. HDFormer significantly outperforms state-of-the-art (SOTA) models on Human3.6M and MPI-INF-3DHP datasets, requiring only 1/10 of the parameters and significantly lower computational costs. Moreover, HDFormer demonstrates broad real-world applicability, enabling real-time, accurate 3D pose estimation. The source code is in https://github.com/hyer/HDFormer