Multivariate time series forecasting plays a crucial role in various fields
such as finance, traffic management, energy, and healthcare. Recent studies
have highlighted the advantages of channel independence to resist distribution
drift but neglect channel correlations, limiting further enhancements. Several
methods utilize mechanisms like attention or mixer to address this by capturing
channel correlations, but they either introduce excessive complexity or rely
too heavily on the correlation to achieve satisfactory results under
distribution drifts, particularly with a large number of channels. Addressing
this gap, this paper presents an efficient MLP-based model, the Series-cOre
Fused Time Series forecaster (SOFTS), which incorporates a novel STar
Aggregate-Dispatch (STAD) module. Unlike traditional approaches that manage
channel interactions through distributed structures, e.g., attention, STAD
employs a centralized strategy. It aggregates all series to form a global core
representation, which is then dispatched and fused with individual series
representations to facilitate channel interactions effectively. SOFTS achieves
superior performance over existing state-of-the-art methods with only linear
complexity. The broad applicability of the STAD module across different
forecasting models is also demonstrated empirically. For further research and
development, we have made our code publicly available at
this https URL

该研究论文提出了一种基于多层感知器的模型（SOFTS），它利用了一个新颖的 STar 聚合调度（STAD）模块，通过将所有系列聚合成一个全局核心表示，再与各个系列表示进行调度和融合，以有效促进通道间的交互，实现了优于现有最先进方法的性能，且具有线性复杂度。

SOFTS: 高效多变量时间序列融合预测

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core  Fusion

Graph convolution networks (GCNs) have achieved remarkable performance in
skeleton-based action recognition. However, existing previous GCN-based methods
have relied excessively on elaborate human body priors and constructed complex
feature aggregation mechanisms, which limits the generalizability of networks.
To solve these problems, we propose a novel Spatial Topology Gating Unit
(STGU), which is an MLP-based variant without extra priors, to capture the
co-occurrence topology features that encode the spatial dependency across all
joints. In STGU, to model the sample-specific and completely independent
point-wise topology attention, a new gate-based feature interaction mechanism
is introduced to activate the features point-to-point by the attention map
generated from the input. Based on the STGU, in this work, we propose the first
topology-aware MLP-based model, Ta-MLP, for skeleton-based action recognition.
In comparison with existing previous methods on three large-scale datasets,
Ta-MLP achieves competitive performance. In addition, Ta-MLP reduces the
parameters by up to 62.5% with favorable results. Compared with previous
state-of-the-art (SOAT) approaches, Ta-MLP pushes the frontier of real-time
action recognition. The code will be available at
this https URL

基于骨骼的动作识别中，提出了一种新颖的基于空间拓扑门控单元（STGU）的多层感知器（MLP）模型，通过捕捉编码所有关节之间空间依赖关系的共同出现的拓扑特征来实现对样本特定且完全独立的逐点拓扑注意力的建模，并在三个大规模数据集上取得了竞争性的性能，提高了 62.5％的参数效率，推动了实时动作识别的前沿。