Multivariate time series forecasting underpins applications in finance, meteorology, and industrial operations. Yet two persistent hurdles remain: (i) models typically choose between Channel–Independent (CI) and Channel–Mixed (CM) formulations—each with distinct strengths—leading to large performance variance across datasets; and (ii) short-term dynamics and long-term trends are hard to model jointly, making it difficult to capture both transient bursts and periodic patterns. We propose FusionTimePatch (FTP), a purely MLP-driven, lightweight framework composed of three modules: (1) Dual-View Global–Local Fusion (Dual-GLF), which runs CI and CM views in parallel and employs multi-scale patch recursion to adaptively adjust the look-back window, thereby coupling global tendencies with local details; (2) Channel Enhancement (CE), which adaptively identifies and amplifies salient channel signals and diffuses them to others, improving sensitivity to abrupt events and latent drivers; and (3) a Linear Fusion layer, which unifies Dual-GLF and CE outputs to strengthen cross-view interactions and enhance robustness. Extensive experiments on multiple public benchmarks show FTP consistently surpasses state-of-the-art counterparts in both accuracy and efficiency, offering a scalable new paradigm for multichannel forecasting. Code and datasets are publicly available at https://github.com/Zhveh7/FTP.

Accurate forecasting of time series mitigates the uncertainty of future outlooks and is a great help in reducing errors in decisions. Despite years of researches, there are still some challenges to accurate forecasting of time series, including the difficulty of dynamic modeling, the problem of capturing short-term correlations, and the conundrum of long-term forecasting. This paper offers an A dversarial Truncated Cauchy S elf- A ttentive T ime- V ariant Neural N etwork (ASATVN) for multi-step ahead time series forecasting. Specifically, the proposed model builds on Generative Adversarial Networks, in which the generator is composed of a novel time-variant model. The time-variant model contributes to learning dynamic time-series changes with its time-variant architecture and employs a newly proposed Truncated Cauchy Self-Attention block to capture the local sequential dependencies better. For the discriminator , two self-attentive discriminators are presented to regularize predictions with fidelity and continuity, which is beneficial to predicting sequence over longer time horizons. Our proposed ASATVN model outperforms the state-of-the-art predictive models on eleven real-world benchmark datasets, demonstrating its effectiveness.