Using multi-scale histograms to answer pattern existence and shape match queries over time series data
Bibliographic record
Abstract
Similarity-based querying of time series data can be categorized as pattern existence queries and shape match queries. Pattern existence queries find the time series data with certain patterns while shape match queries look for the time series data that have similar movement shapes. Existing proposals address one of these or the other. In this paper, we propose multi-scale time series histograms that can be used to answer both types of queries, thus offering users more flexibility. Multiple histogram levels allow querying at various precision levels. Most importantly, the distances of time series histograms at lower scale are lower bounds of the distances at higher scale, which guarantees that no false dismissals will be introduced when a multi-step filtering process is used in answering shape match queries. We further propose to use averages of time series histograms to reduce the dimensionality and avoid computing the distances of full time series histograms. The experimental results show that multi-scale histograms can effectively find the patterns in time series data and answer shape match queries, even when the data contain noise, time shifting and scaling, or amplitude shifting and scaling.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".