Timebinning

Timebinning is a data processing technique used to summarize time-stamped data by grouping observations into fixed-length intervals, or bins, and computing an aggregate statistic within each bin. It is widely used to convert irregularly spaced events into a regularly sampled time series and to quantify rates, counts, or averages over time.

To perform timebinning, a reference time t0 and a bin width delta_t are chosen. Each event with timestamp t is assigned to the bin with index floor((t - t0)/delta_t). The contents of each bin are then summarized, for example by counting events, summing values, or averaging measurements, producing a sequence of bin centers or edges with corresponding statistics.

Applications span many fields. In astronomy and particle physics, timebinning converts photon arrival times or event streams into light curves or event-rate estimates. In network monitoring and finance, it aggregates tick data into regular time series. In neuroscience and environmental sensing, binning estimates firing rates or sensor averages over time.

Variants and considerations include fixed versus adaptive binning, where bin widths vary to match data density or variability scales. Edge handling, partial bins at the start or end, and bin alignment can affect results. When counting events, Poisson errors are common, while rate estimates require normalization by bin duration. Bin width choice involves a trade-off between temporal resolution and statistical precision; too wide bins smooth away variability, too narrow bins increase noise.

Practical use often relies on software tools such as time-based resampling or group-by operations in data analysis libraries. Choosing binning parameters should reflect the temporal structure of the underlying process and the analysis goals.