Time-causal and time-recursive wavelets
This work introduces a framework for time-causal wavelet analysis to process temporal signals in real-time where future data is unavailable. It develops time-causal wavelets based on derivatives of a special smoothing kernel, allowing signal decomposition across different temporal scales while strictly maintaining causality. The method uses efficient recursive filters for fast, low-resource implementation and provides exact reconstruction methods.