Efficient and Scalable Techniques for Multivariate Time Series \nAnalysis and Search
Bibliographic record
Abstract
Innovation and advances in technology have led to the growth of time series data at a phenomenal rate in many applications. Query processing and the analysis of time series data have been studied and, numerous solutions have been proposed. In this research, we focus on multivariate time series (MTS) and devise techniques for high dimensional and voluminous MTS data. \nThe success of such solution techniques relies on effective dimensionality reduction in a preprocessing step. Feature selection has often been used as a dimensionality reduction technique. It helps identify a subset of features that capture most characteristics from the data. We propose a more effective feature subset selection technique, termed Weighted Scores (WS), based on statistics drawn from the Principal Component Analysis (PCA) of the input MTS data matrix. The technique allows reducing the dimensionality of the data, while retaining and ranking its most influential features. We then consider feature grouping and develop a technique termed FRG (Feature Ranking and Grouping) to improve the effectiveness of our technique in sparse vector frameworks. We also developed a PCA based MTS representation technique M2U (Multivariate to Univariate transformation) which allows to transform the MTS with large number of variables to a univariate signal prior to performing downstream pattern recognition tasks such as seeking correlations within the set. \nIn related research, we study the similarity search problem for MTS, and developed a novel correlation based method for standard MTS, ESTMSS (Efficient and Scalable Technique for MTS Similarity Search). For this, we uses randomized dimensionality reduction, and a threshold based correlation computation. The results of our numerous experiments on real benchmark data indicate the effectiveness of our methods. \nThe technique improves computation time by at least an order of magnitude compared to other techniques, and affords a large reduction in memory requirement while providing comparable accuracy and precision results in large scale frameworks.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".