Cross-validation based forecasting method: a machine learning approach
Bibliographic record
Abstract
Our paper aims to evaluate two novel methods on selecting the best forecasting model or its combination based on a Machine Learning approach. The methods are based on the selection of the ”best” model, or combination of models, by crossvalidation technique, from a set of possible models. The first one is based on the seminal paper of Granger-Bates (1969) but weights are estimated by a process of cross-validation applied on the training set. The second one selects the model with the best forecasting performance in the process described above, which we called CvML (Cross-Validation Machine Learning Method). The following models are used: exponential smoothing, SARIMA, artificial neural networks and Threshold autoregression (TAR). Model specification is chosen by R packages: forecast and TSA. Both methods – CvML and MGB - are applied to these models to generate forecasts from one up to twelve periods ahead. Frequency of data is monthly. We run the forecasts exercise to the following to monthly series of Industrial Product Indices for seven countries: Canada, Brazil, Belgium, Germany, Portugal, UK and USA. The data was collected at OECD data, with 504 observations. We choose Average Forecast Combination, Granger Bates Method, MCS model, Naive and Seasonal Naive Model as benchmarks.Our results suggest that MGB did not performed well. However, CvML had a lower mean absolute error for most of countries and forecast horizons, particularly at longer horizons, surpassing all the proposed benchmarks. Similar results hold for absolute mean forecast error.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".