Timealignment

Time alignment, also referred to as time alignment of signals or data, is the process of adjusting the time references of signals, events, or datasets so that temporally corresponding features align across sources. It is essential for comparison, fusion, and joint analysis of data collected with different sensors, modalities, or sampling schemes. The goal is to establish a common time base and compensate for delays, jitter, or clock drift that cause misalignment.

Techniques used for time alignment include cross-correlation to estimate and correct fixed time offsets; phase correlation in the frequency domain; dynamic time warping to accommodate non-linear time distortions; and resampling or interpolation to a common sampling rate. In distributed systems and communications, time synchronization protocols such as the Network Time Protocol (NTP) or the Precision Time Protocol (PTP) are used to align clocks, while event-based alignment relies on markers or shared triggers.

Applications span audio-visual synchronization, multi-sensor fusion in robotics and autonomous systems, neuroscience experiments (EEG/MEG), medical imaging gating, seismology, videography, and financial market data alignment across venues.

Challenges include clock drift, jitter, missing data, variable network delays, and asynchronous sampling. Evaluation typically uses alignment error metrics, such as the residual offset after correction or dynamic time warping–based costs. Robust methods may incorporate uncertainty models and regularization to avoid overalignment.