TSEA: An Open Source Python-Based Annotation Tool for Time Series Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present the Time Series Event Annotator (TSEA), a graphical user interface annotation tool for time series data that enables rapid visualization, labeling, and annotation of signals, including individual points and ranges. Time series data are common to a variety of applications. Oftentimes there is a need to label segments and/or points of the signals, highlighting important elements that are later used for feature extraction or for signal analysis. A number of illustrative applications of the developed tool are discussed, particularly for the detection of "R" peaks from electrocardiogram signals. While algorithms for detection of "R" peaks can achieve good results when applied to an electrocardiogram signal with a high signal-to-noise ratio, they often lead to incorrect detections in the presence of noise or motion artifact commonly found in clinical setups. In such cases, the Time Series Event Annotator (TSEA) enables efficient imputing of missed or incorrect "R" peak detections, leading to increased data integrity for downstream analysis, at minimum cost. Considering that data cleaning often represents the majority of effort when developing a new machine learning pipeline, our annotation tool will accelerate the development of a wide range of new machine learning applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it