Patch-Level Contrastive Learning for Improved Time Series Classification with Gramian Angular Field

Time series classification (TSC) presents significant challenges in data analytics and plays an essential role in industries such as manufacturing, healthcare, and finance. Existing research has utilized sequence models, such as long short-term memory (LSTM) and transformers, to achieve high performance; however, these models fail to fully address the inherent challenges of capturing long-term dependencies in time series data. Recently, efforts have been made to overcome these limitations by processing time series data through conversion into images. One such method, the Gramian Angular Field (GAF), converts time series data into images that visually represent the relationship between time points, effectively capturing global trends that traditional sequence models often miss. However, single-modality approaches, which rely on only one representation of time series data, still face limitations in comprehensively capturing the diverse features of a time series. Additionally, existing multi-modality approaches may overlook the detailed features between time points. To address these issues, this article proposes a patch-level hybrid contrastive learning model that combines transformer with Vision Transformer (ViT). The proposed model converts a one-dimensional time series into GAF images and leverages contrastive learning to robustly learn fine-grained features. This enables the model to effectively capture both temporal relationships between time points and spatial relationships between patches, thereby enhancing its generalization capabilities. Experimental results demonstrate that the proposed model outperforms the existing methods on the UCR TSC dataset. This shows that the model can overcome the limitations of single-modality approaches by integrating the complex structural features of time series data through a multi-modality framework, ultimately leading to improved classification performance. This approach provides a new avenue for enhancing TSC and holds promise for applications across various industries.