I scan your continuous measurement data to find hidden signals, classify what kind of structure is in each frequency range, and pinpoint the exact moment when the system behavior changes.
I Will Detect Hidden Signal Structures and Sudden Transitions in Your Time Series Data
Single dataset diagnostic report with automated band scanning and classification.
- Automated frequency band scanning
- Five-class classification
- Transition detection
- 3-panel visualization
- CSV results table
- 1 round of Q&A
Up to 3 datasets, full diagnostics with cross-dataset comparison.
- All Basic features
- Cross-dataset structural comparison
- Transition timestamp comparison
- Up to 2 Q&A rounds
Batch analysis for up to 10 datasets with extended reporting.
- All Standard features
- Batch processing (up to 10 datasets)
- Extended analysis report
- Up to 3 Q&A rounds
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Key details about this service to help you decide. Generated by Zinn Hub, not the seller.
Value Position
Analysis Type
Supported Data
Validated On
Deliverables
What You'll Receive
Full Description
Time Series Structure Diagnostics
I scan your single-channel continuous data, identify which frequency bands contain structure, classify each band, and detect the exact moment when the system behavior changes.
Most common analysis tools tell you "how much power is at each frequency." This service tells you "what kind of structure is at each frequency, and when it changed."
Based on a published preprint (DOI: 10.5281/zenodo.22837806).
What you get
- A diagnostic report (PDF) with per-band metrics and classification
- A structured results table (CSV) with all band metrics and labels
- Detected transition timestamps
- Three-panel visualization (raw signal, CV over time, entropy over time)
- A short methodology note explaining how to read the results
What we do NOT provide
- Algorithm source code
- Exact parameter values
- Tuning methods or implementation details
The core algorithm and parameter settings are retained as commercial intellectual property. Only analysis results are delivered.
Validated on
- Synthetic signals with known structure
- LIGO gravitational-wave detector data
- Real-world continuous measurements from multiple domains
Potential applications
- EEG and neuroscience: meditation, sleep staging, anesthesia monitoring, neurological research
- Mechanical engineering: vibration monitoring, equipment fault detection, rotating machinery
- Finance and economics: cycle analysis, regime shift detection, structural breaks
- Environmental science: climate records, hydrological series, seismic data
- Any single-channel continuous dataset where the behavior may change over time
What you need to provide
- Your data file (CSV, TXT, or HDF5)
- Sampling rate (how often data is recorded)
- A short description of what you are looking for
Delivery time
- Single file: within 24 hours
- Batch (10+ files): 3โ5 business days
Pricing
- Basic: one dataset, report + CSV
- Standard: up to 3 datasets, comparative analysis, one Q&A round
- Advanced: batch analysis, custom frequency bands (no source code)
Data privacy
Your uploaded data is used only for your analysis task. After delivery, source data is permanently deleted within 7 days. Data is never shared or used for any other purpose.
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Compare Packages
| ็น่ฒ | Basic Diagnosis | Standard Diagnosis | Advanced Diagnosis |
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
| Revisions | 1 | 2 | 3 |
| Automated frequency band scanning | โ | โ | โ |
| Five-class classification (signal / segmented / white_noise / nonstationary / signal_low) | โ | โ | โ |
| Transition detection | โ | โ | โ |
| 3-panel visualization (raw signal, CV, entropy) | โ | โ | โ |
| CSV results table | โ | โ | โ |
| Cross-dataset structural comparison | โ | โ | โ |
| Transition timestamp comparison | โ | โ | โ |
| 2 rounds of Q&A | โ | โ | โ |
| Batch processing | โ | โ | โ |
| Extended analysis report (additional metrics and comparisons) | โ | โ | โ |
| 3 rounds of Q&A | โ | โ | โ |
Samples
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Portfolio
Examples of the seller's work related to this Zinn.
LIGO O4c1 Noise Structure Analysis
Applied the diagnostic tool to 24 minutes of LIGO O4c1 continuous data. Detected 4 transient events and quantified the noise structure baseline.
Extra Information
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Frequently Asked Questions
No. This is a diagnostic tool, not a forecasting tool. It analyzes historical/current data to detect structural changes and state transitions. It cannot predict future behavior.
No. The tool outputs statistical structure metrics and class labels. It does NOT reconstruct the exact signal shape or waveform.
No. Currently, the tool supports single-channel time series only. It does not perform multi-sensor fusion or spatial localization.
No. This is an offline analysis tool designed for historical data and batch processing. It is not designed for real-time streaming.
The method uses sliding window analysis. Due to window smoothing effects, transitions may be detected slightly after the true event. This is normal for window-based detection and will be noted in the report.
No. This is a service-based listing. You will receive the diagnostic report, CSV tables, and PNG visualizations, but the source code (Python scripts) is NOT included in any package.
The report is in English. Chinese-language reports are available on request. Please specify your preferred language when ordering.
Yes. Your uploaded data is used only for your analysis task. After delivery, your source data is permanently deleted within 7 days. Your data is never shared, published, or used for any other purpose.
If you need a signed NDA before sharing data, contact me before placing the order. NDA signing is available for academic and industrial clients.
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