Univariate Channel Fusion for Multivariate Time Series Classification

2026-04-17 · Fernando Moro, Vinicius M. A. Souza arxiv

Multivariate time series classification (MTSC) plays a crucial role in various domains, including biomedical signal analysis and motion monitoring. However, existing approaches, particularly deep learning models, often require high computational resources, making them unsuitable for real-time applications or deployment on low-cost hardware, such as IoT devices and wearable systems. In this paper, we propose the Univariate Channel Fusion (UCF) method to deal with MTSC efficiently. UCF transforms multivariate time series into a univariate representation through simple channel fusion strategies such as the mean, median, or dynamic time warping barycenter. This transformation enables the use of any classifier originally designed for univariate time series, providing a flexible and computationally lightweight alternative to complex models. We evaluate UCF in five case studies covering diverse application domains, including chemical monitoring, brain-computer interfaces, and human activity analysis. The results demonstrate that UCF often outperforms baseline methods and state-of-the-art algorithms tailored for MTSC, while achieving substantial gains in computational efficiency, being particularly effective in problems with high inter-channel correlation.

📄 PDF Abstract BibTeX arXiv:2604.16119

Code (0)

등록된 구현이 없습니다.

Tasks

Time Series Classification Computational Efficiency

Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series

2024-11-19 · Mingzhu Liu, Angela H. Chen, George H. Chen

Time series foundation models are pre-trained on large datasets and are able to achieve state-of-the-art performance in diverse tasks. However, to date, there has been limited work demonstrating how well these models per…

Time Series Time Series Prediction

Channel masking for multivariate time series shapelets

2017-11-02 · Dripta S. Raychaudhuri, Josif Grabocka, Lars Schmidt-Thieme

Time series shapelets are discriminative sub-sequences and their similarity to time series can be used for time series classification. Initial shapelet extraction algorithms searched shapelets by complete enumeration of …

General Classification Time Series Time Series Analysis Time Series Classification

MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer

2024-03-14 · Wenyong Han, Tao Zhu Member, Liming Chen, Huansheng Ning 외

The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a pr…

Multivariate Time Series Forecasting Time Series Time Series Forecasting

The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

2023-04-11 · Lu Han, Han-Jia Ye, De-Chuan Zhan

Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predict future values. However, recently, the…

Multivariate Time Series Forecasting Time Series Time Series Forecasting

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models

2026-02-25 · Jinpeng Li, Zhongyi Pei, Huaze Xue, Bojian Zheng 외 arxiv

Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasting remains challenging. To address this,…