Time Series Motif Discovery and Anomaly Detection Based on Subseries Join
Bibliographic record
Abstract
Abstract — Time series are composed of sequences of data items measured at typically uniform intervals. Time series arise frequently in many scientific and engineering applications, including finance, medicine, digital audio, and motion capture. Time series motifs are repeated similar subseries in one or multiple time series data. Time series anoma-lies are unusual subseries in one or multiple time se-ries data. Finding motifs and anomalies in time series data are closely related problems and are useful in many domains, including medicine, motion capture, meteorology, and finance. This paper presents a novel approach for both the motif discovery problem and the anomaly detection problem. First, we use a subseries join operation to match similar subseries and to obtain similarity rela-tionships among subseries of the time series data. The subseries join algorithm we use can efficiently and ef-fectively tolerate noise, time-scaling, and phase shifts. Based on the similarity relationships found among subseries of the time series data, the motif discovery and anomaly detection problems can be converted to graph-theoretic problems solvable by known graph-theoretic algorithms. Experiments demonstrate the effectiveness of the proposed approach to discover motifs and anomalies in real-world time series data. Experiments also demonstrate that the proposed ap-proach is efficient when applied to large time series datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".