Work

ManyROCKET: Adaptive parallel multi-representation MiniROCKET for time series classification

Year: 2026

Type: article

Abstract: Time series classification has achieved strong performance through transformation-based methods such as MiniROCKET, which rely on fast random convolutional feature extraction from raw temporal signals. However, representing time series data in a single domain limits the ability to capture complex temporal–spectral and structural characteristics in diverse real-world datasets. In this study, we propose ManyROCKET, an adaptive parallel MiniROCKET framework that integrates multiple complementary signal representations within a unified and computationally efficient classification pipeline. The framework incorporates seven established transformations spanning time-domain, frequency-domain, time–frequency, analytic, and derivative-based representations. By systematically combining these transformations, we explore a broad set of multi-representation configurations on benchmark datasets from the UCR Time Series Archive to identify the most effective representation subset. The selected configuration is then evaluated at archive scale and directly compared with the Raw Time (Identity) MiniROCKET baseline. To ensure scalability, we introduce an adaptive two-tier parallel execution strategy that enables concurrent processing of multiple representations while preserving the core efficiency of MiniROCKET-style feature extraction. Extensive experimental results demonstrate that multi-representation learning consistently and robustly outperforms the standard raw time-domain MiniROCKET baseline across diverse datasets. The proposed ManyROCKET framework achieves superior classification performance while maintaining practical computational efficiency, highlighting the effectiveness of integrating complementary signal domains within a unified architecture.

Source: Journal of Computational Design and Engineering

DOI: 10.1093/jcde/qwag087

Authors: Mehmet Kurnaz, Celal Alagöz

Institution: Sivas Bilim ve Teknoloji Üniversitesi

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,650

Funder: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu