Bibliographic record
Abstract
This paper presents a time series analysis of the Toronto-Dominion Bank stock (TD) for 10 months into the future. The data set was collected from Yahoo Finance. The time period of the dataset is from January 2008 to November 2015 and the prediction goes till September 2016. The attribute measured is the value of the stock measured at the beginning of the month. The data was analyzed using the TSA package in the R statistical language. Exploring the data through multiple tests gave five possible models that could represent the trends in the data. The model's predictions were then compared using MPE, MSE, MAE, and MAPE to find the prediction accuracy. The Regression AR(1) model was clearly the best model after looking at these values. This model also did not have insignificant coefficients that were an issue for the other models. The value of a company is the results of many factors both external and internal, despite this, the Regression AR(1) model was still able to give fairly accurate predictions 10 months into the future. Discipline: Statistics Faculty Mentor: Dr. Cristina Anton
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".