Gramian Angular Field

Gramian Angular Fields (GAFs) are a data transformation technique that converts one-dimensional time series data into two-dimensional images, preserving spatiotemporal relationships and making them suitable for image processing and deep learning methods like Convolutional Neural Networks (CNNs). Current research focuses on applying GAFs to diverse fields, including autonomous vehicle behavior analysis, financial time series forecasting, and anomalous diffusion characterization, often integrating them with advanced architectures such as Vision Transformers and U-Nets. This approach enhances the analysis and classification of complex temporal data, leading to improved accuracy in various applications ranging from anomaly detection in medical signals (e.g., ECG) to fault diagnosis in industrial machinery.