Work

Early time-series classification with SPRT and normalizing flow

Year: 2025

Type: article

Abstract: This paper proposes a novel approach for early time-series classification, addressing the trade-off between prediction accuracy and earliness, which is critical in real-time applications. The Sequential Probability Ratio Test (SPRT) provides an optimal solution but relies on prior knowledge of the data’s probability distribution, which is an assumption often impractical in real-world scenarios. Existing studies commonly assume a normal distribution, which limits classification performance in complex data. To overcome this limitation, we integrate normalizing flow into the SPRT framework, enabling the estimation of conditional probability distributions through a series of invertible transformations. This approach allows for precise probability estimation, improving the accuracy of early classification. Experimental results on a preprocessed dataset demonstrate that the proposed model significantly enhances classification performance, offering a promising direction for advancing early time-series classification.

Source: ICT Express

DOI: 10.1016/j.icte.2025.09.015

Authors: Jun Hee Jo, Kae Won Choi

Institution: Sungkyunkwan University

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion