MSTN: Fast and Efficient Multivariate Time Series Model

2025-11-25

AI Summary

Real-world multivariate time series exhibit strong non-stationarity and cross-scale dynamics; however, prevailing models rely on fixed-scale priors—such as chunk-based tokenization or static frequency-domain transformations—limiting modeling flexibility and hindering robustness to abrupt, high-magnitude events. To address this, we propose an adaptive hierarchical architecture that jointly captures instantaneous fluctuations and long-term trends via multi-scale convolutional encoding, integrates sequential modeling using BiLSTM or Transformer backbones, incorporates Squeeze-and-Excitation gating for channel-wise feature recalibration, and employs multi-head temporal attention for context-aware dynamic feature fusion. The resulting framework establishes a unified paradigm for time-series modeling. Evaluated across 32 benchmark datasets on forecasting, imputation, and classification tasks, our method achieves state-of-the-art performance on 24 datasets—outperforming leading approaches including EMTSF, TimesNet, and PatchTST by significant margins.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Real-world time-series data is highly non stationary and complex in dynamics that operate across multiple timescales, ranging from fast, short-term changes to slow, long-term trends. Most existing models rely on fixed-scale structural priors, such as patch-based tokenization, fixed frequency transformations, or frozen backbone architectures. This often leads to over-regularization of temporal dynamics, which limits their ability to adaptively model the full spectrum of temporal variations and impairs their performance on unpredictable, Sudden, high-magnitude events. To address this, we introduce the Multi-scale Temporal Network (MSTN), a novel deep learning architecture founded on a hierarchical multi-scale and sequence modeling principle. The MSTN framework integrates: (i) a multi-scale convolutional encoder that constructs a hierarchical feature pyramid for local patterns (ii) a sequence modeling component for long-range temporal dependencies. We empirically validate this with BiLSTM and Transformer variants, establishing a flexible foundation for future architectural advancements. and (iii) a gated fusion mechanism augmented with squeeze-and-excitation (SE) and multi-head temporal attention (MHTA) for dynamic, context-aware feature integration. This design enables MSTN to adaptively model temporal patterns from milliseconds to long-range dependencies within a unified framework. Extensive evaluations across time-series long-horizon forecasting, imputation, classification and generalizability study demonstrate that MSTN achieves competitive state-of-the-art (SOTA) performance, showing improvements over contemporary approaches including EMTSF, LLM4TS, HiMTM, TIME-LLM, MTST, SOFTS, iTransformer, TimesNet, and PatchTST. In total, MSTN establishes new SOTA performance on 24 of 32 benchmark datasets, demonstrating its consistent performance across diverse temporal tasks.

Problem

• Modeling highly non-stationary multivariate time series data
• Addressing limitations of fixed-scale structural priors in temporal modeling
• Adaptively capturing multi-scale dynamics from short-term to long-term patterns

Innovation

• Multi-scale convolutional encoder for hierarchical feature pyramid
• Sequence modeling component for long-range dependencies
• Gated fusion with SE and MHTA for dynamic integration