Deep State Space

Deep state-space models combine the representational power of deep learning with the interpretability and efficiency of state-space models to analyze complex time-series data. Current research focuses on applying these models, often utilizing recurrent neural networks like LSTMs and GRUs, or novel architectures like Temporal Kolmogorov-Arnold Networks, to diverse applications including time series forecasting, event-based sensor processing, and healthcare data analysis. This approach allows for the modeling of intricate, potentially multimodal dynamics and the extraction of meaningful latent states, leading to improved prediction accuracy and enhanced understanding of underlying processes in various fields. The resulting models offer both improved performance and increased interpretability compared to traditional methods.