Time Invariance

Time invariance, the property of a system or model remaining unchanged over time, is a crucial concept across diverse scientific fields. Current research focuses on developing methods to either exploit or overcome time invariance, depending on the application. This includes designing models that explicitly incorporate temporal dependencies (e.g., using assorted-time normalization in recurrent neural networks) or those that are inherently invariant to temporal shifts (e.g., periodic graph transformers for material science or universal Fourier attacks for time series). Achieving robust time invariance or effectively managing its absence has significant implications for improving forecasting accuracy (e.g., in electricity pricing), enhancing representation learning in various domains (e.g., image processing and material science), and developing more resilient systems against adversarial attacks.