Time normalization is a research area that focuses on standardizing and aligning the timestamps or time intervals of data points, particularly in the context of time-series data or sequences. This process is crucial for dealing with temporal data that may be recorded at different rates, have varying time intervals, or contain missing or irregular timestamps. Time normalization techniques aim to make the temporal aspects of the data consistent and comparable, allowing for meaningful analysis and interpretation across different datasets or data sources. This can involve interpolation, resampling, time warping, or other methods to adjust timestamps and ensure uniformity in the temporal structure of the data. Time normalization is commonly used in fields such as signal processing, machine learning, and data analysis to improve the accuracy and reliability of temporal data analysis and modeling.