Periodic pattern analysis of non-uniformly sampled stock market data
Bibliographic record
Abstract
Periodic pattern detection is an important data mining task that highlights the temporal regularities within the data. It aims at finding if a partial or full pattern has a cyclic repetition in the considered time series or data sequence. Periodicity is found in large number of datasets including m eteorological data, transaction count, computer network traffic, power consumption, sunspots, Electrocardiography (ECG), biological sequences such as DNA and protein [33]. Periodic pattern analysis not only helps in understanding the behavior of the data but also contributes in predicting the future trends of the data. There are several algorithms reported in the literature for periodicity detection in time series and biological sequences [3,34] but none of these algorithms discuss the non-uniformly sampled data. General assumption in the time series and sequence data is that the consecutive data values are sampled at regular or uniform interval of time. But this assumption hardly holds in real datasets; for example the stock market data analyzed in this paper record various features for each working day. This data has a quite a few missing values for weekly and arbitrary holidays. Although handling this issue is not very complex but requires careful handling. In this paper we analyze the stock market data in detail and show how the periodic pattern analysis may provide the understanding of the data to predict the future trends. Our experimental results show that consideration of missing values in stock market data results in much larger number of interesting results than the trivial periodicity detection approach ignoring the missing values.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".