Temporal Decomposition

Temporal decomposition involves separating complex time-series data into simpler, interpretable components to improve analysis and prediction. Current research focuses on developing novel algorithms and model architectures, such as diffusion models, neural networks (including those incorporating polynomial approximations and autoencoders), and Gaussian processes, to achieve more robust and efficient decomposition across diverse data types, including video, sensor readings, and knowledge graphs. These advancements enhance the accuracy and interpretability of various applications, ranging from anomaly detection in crowd surveillance to improved video compression and the generation of synthetic industrial data for large model training.