Periodic pattern analysis of non-uniformly sampled stock market data
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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