Data and analytics · delivered in 5-10 days
This is a diagnostic service for single-channel time-series data. Without needing labelled training examples, fixed templates or a physical model, it automatically locates the frequency bands that carry structure, sorts each band into one of five categories, flags abrupt shifts in behaviour, and lets you compare separate time periods against each other. The classification draws on four independent statistical measures: coefficient of variation, sample entropy, envelope skewness and recurrence rate. The underlying method is described in an academic preprint held on Zenodo, DOI 10.5281/22837806.
A structured CSV table listing every band's metrics and label, together with a methodology note that walks through the analysis steps, the parameters used, and how to interpret the output.
The report also states the precise point at which the system's behaviour changed, for instance noting that at t=568 seconds the pattern moved from a stable state into an unstable one.
It cannot rebuild the original waveform, cannot pinpoint a physical source, cannot forecast what happens next, cannot handle multi-channel inputs, cannot process series shorter than 500 points, and cannot cope with a strong trend unless the data has been detrended beforehand.
1. A time-series file in CSV, TSV, TXT or HDF5 format, containing at least one numeric column (a timestamp column is optional), with a minimum length of 1000 points and 5000 or more recommended.
2. The sampling rate, whether that is one reading per second, one per millisecond, or something like a 16384 Hz instrument feed.
You have one long, single-channel series, you suspect its behaviour changed at some stage, you want to separate a genuine structural shift from a passing glitch, or you need an automated first pass before deeper investigation.
Real-time streaming, arrays of multiple sensors, requirements for physical explanations, fault localisation, or forward-looking prediction all fall outside its scope.
Your order opens its own thread here the moment it is paid, and everything about that order - questions, changes and the final report - happens in it.
No. This service diagnoses patterns in data you already hold; it identifies structural shifts and state changes rather than forecasting what comes next.
No. Output is limited to statistical descriptors (coefficient of variation, entropy, skewness, recurrence rate) and the five-class labels. The original waveform shape is not reconstructed.
No. The tool works with single-channel series only and does not combine multiple sensors or provide spatial localisation.
No. This is an offline, batch-oriented analysis built for stored data, not for continuous real-time streams.
The method relies on sliding windows (for example a 32-second window with an 8-second step), so smoothing can push a detected peak slightly after the true event, such as a real change at t=16s showing up around t=22s. This is expected and is noted in the report.
No. This listing covers the diagnostic report, CSV tables and PNG charts; the underlying Python scripts are not part of any package.
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