Periodicity data mining in time series using Suffix Arrays
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
This research paper focuses on data mining in time series and its applications on financial data. Data-mining attempts to analyze time series and extract valuable information about pattern periodicity, which might be concealed by substantial amounts of unformatted, random information. Such information, however, is of great importance as it can be used to forecast future behavior. In this paper, a new methodology is introduced aiming to utilize Suffix Arrays in data mining instead of the commonly used data structure Suffix Trees. Although Suffix Arrays, normally, require high storage capacity, the algorithm proposed allows them to be constructed in linear time. The methodology is also extended to detect repeated patterns in time series with time complexity of. This, combined with the capability of external storage, creates a critical advantage, for an overall efficient data mining and analysis regarding the construction of time series data structure and periodicity detection. The test results, presented below demonstrate the applicability and effectiveness of the proposed technique.
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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 | 0.000 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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