peakwindows

Peakwindows is a term used in time-series analysis to describe a family of window-based peak detection methods and, in some contexts, a software library that implements them. The central idea is to apply localized windowing to a signal in order to identify and quantify peaks while controlling for noise and resolution.

In practice, peakwindows analysis segments data with overlapping or adaptive windows. Within each window, a peak is declared when the signal exceeds a local threshold and stands out relative to neighboring samples. The approach can support different window shapes and scales, enabling multi-resolution detection to accommodate peaks of varying widths. Outputs typically include peak times, amplitudes, widths, and optional confidence scores.

Software implementations of peakwindows usually provide a function or module that accepts a time-series array, sampling rate, and user-specified parameters such as minimum peak height, minimum peak distance, and an optional window size schedule. Results are commonly returned as a list or table of detected peaks, with accompanying metadata suitable for downstream analysis or visualization. Many implementations also offer export formats (CSV, JSON) and integration with popular data environments such as Python, MATLAB, and R.

The concept emerged from broader work on windowed analysis and peak picking in fields like chromatography, spectroscopy, and signal processing. Peakwindows is used in disciplines including mass spectrometry, chromatography, seismology, and finance to identify significant events within noisy data streams.

Limitations include sensitivity to parameter choices (thresholds, window sizes, and scales) and the potential for missed or spurious peaks if settings are not tuned to the data. See also peak detection, window function, and time-series analysis.