Irregularly Spaced Time Series Data with Time Scale Measurement Error
Bibliographic record
Abstract
This project can be mainly divided into two sections. In the first section it attempts to model an irregularly spaced time series data where time scale is being measured with a measurement error. Modelling an irregularly spaced time series data alone is quite challenging as traditional time series techniques only capture equally/regularly spaced time series data. In addition to that, the measurement error in the time scale make it even more challenging to incorporate measurement error models and functional approaches to model the time series. Thus, this project is based on a Bayesian approach to model a flexible regression function when the time scale is being measured with a measurement error. The regression functions are modelled with regression P-splines and the exploration of posterior is carried out using a fully Bayesian method that uses Markov chain monte carlo (MCMC) techniques. In section two, we identify the relationship/dependency between two irregularly spaced time series data sets which were modelled using regression P-splines and a fully Bayesian method, using windowed moving correlations. The validity of the suggested methodology is then explored using two simulations. It is then applied on two irregularly spaced time series data sets each subjected to measurement errors in time scale to identify the dependency between them in terms of statistically significant correlations.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".