Change-Point Detection in Business Cycles using Machine Learning Algorithms
Bibliographic record
Abstract
Turning points in business cycles are defined as the onset of a recession or an expansion which are quite difficult to be predicted. In this thesis, we approach the problem of turning (change) point detection as the viewpoint of binary classification task. Due to the small ratio of changes to total data (as the number of recessions is relatively low), we face heavily class-imbalance challenge in this problem. We explore a wide variety of machine learning-based solutions for this problem: from base classifier to the multi-step classifier ensemble algorithm as well as a feature selection step. We examined the proposed classification methods on Canadian large dataset. Among different examined methods, the hybrid ensemble method including data sampling followed by a feature selection and multi-step ensemble can predict the Covid19 recession’s changepoints precisely with all the time series available one month ago. Some robustness checks such as the effect of window size on the model performance are also provided. Moreover, excluding the financial crisis from the training set, the method 8 is still able to predict the changepoints in the case of financial crisis precisely, however, in the case of the Covid-19 recession, they were detected one-period late, suggesting importance of financial crisis’ data in detecting Covid-19 change points.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".