MSTN: Fast and Efficient Multivariate Time Series Prediction Model

Abstract

Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behaviour expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends. However, many contemporary architectures impose rigid, fixed-scale structural priors -- such as patch-based tokenization, predefined receptive fields, or frozen backbone encoders -- which can over-regularize temporal dynamics and limit adaptability to abrupt high-magnitude events. To handle this, we introduce the \emph{Multi-scale Temporal Network} (MSTN), a hybrid neural architecture grounded in an \emph{Early Temporal Aggregation} principle. MSTN integrates three complementary components: (i) a multi-scale convolutional encoder that captures fine-grained local structure; (ii) a sequence modeling module that learns long-range dependencies through either recurrent or attention-based mechanisms; and (iii) a self-gated fusion stage incorporating squeeze-excitation and multi-head attention to dynamically modulate cross-scale representations. This design enables MSTN to flexibly model temporal patterns spanning milliseconds to extended horizons, while avoiding the computational burden typically associated with long-context models. Across extensive benchmarks covering forecasting, imputation, classification, and cross-dataset generalization, MSTN consistently delivers state-of-the-art performance, outperforming recent leading approaches including TIME-LLM, HiMTM, SOFTS, LLM4TS, TimesNet, and PatchTST, and establishing new best results on 24 out of 32 datasets. Despite its strong performance, MSTN remains lightweight and supports fast inference, making it well suited for deployment on edge devices and resource-constrained environments.