George Box's contributions to time series analysis and forecasting
Bibliographic record
Abstract
George Edward Pelham Box was born on October 19, 1919 in Gravesend, Kent, UK and died on March 28, 2013 in Madison, Wisconsin, USA. George Box made significant contributions to many fields of statistics including design of experiments and response surface methodology, evolutionary operation, statistical inference, robustness, Bayesian methods, time series analysis and forecasting, and quality improvement. Our paper discusses his contributions to time series analysis and forecasting. His work in this area started in collaboration with Gwilym Jenkins in the early 1960s and continued over the next several decades. His contributions include the classic and extraordinarily influential book ‘Time Series Analysis: Forecasting and Control’ written with Gwilym Jenkins and first published by Holden Day in 1970. Subsequent contributions to time series analysis include joint work with George Tiao, Gregory Reinsel, Daniel Pena, and many former graduate students. His work provided a unified framework for carrying out time series analysis in practice and laid the foundation for many new developments in the field. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".