Build a Lightweight Dataset and Concept Drift Detection Method for Evolving Time Series Data Streams
Bibliographic record
Abstract
Time series forecasting, a potent tool for predicting real-world entities such as financial markets and weather patterns, often grapples with the issue of concept drift, characterized by changes in the behaviour of the time series over time.This study aims to develop a lightweight time series model, efficient in training time, to match the data stream's arrival rate.Furthermore, a method to detect the presence of concept drift in the data stream, regardless of the time point, is discussed.Presented herein is a benchmark dataset, publicly accessible and specifically designed to simulate changing time series scenarios across diverse industries including Energy, Air Quality, and Pollution.This dataset amalgamates synthetic and actual time series along with ground truth concept drift locations, facilitating a comprehensive evaluation of concept drift detection techniques.A novel, lightweight concept drift detection method, which integrates supervised methodologies with statistical metrics to surmount the resource constraints often encountered in streaming data scenarios, is proposed.This method minimizes computational overhead while ensuring reliable drift detection in response to shifting data distributions.Experimental results indicate that the proposed approach surpasses prior methods in computational performance whilst accurately identifying idea drifts in evolving time series data streams.The study contributes a valuable dataset and a lightweight feature selection method, advancing the knowledge in the field of concept drift detection within the context of time series data streams.These advancements provide an efficient technique for tracking changing data patterns across various application domains, thus offering significant implications for future research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 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".