Support Vector Machine for Demand Forecasting of Canadian Armed Forces Spare Parts
Why this work is in the frame
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Bibliographic record
Abstract
The need to reduce inventory costs and increase system operational availability is the main motivation behind improving forecast accuracy of military spare parts demand. In this paper, we assess the potential of Support Vector Machine (SVM) approach for forecasting the demand of Canadian Armed Forces (CAF) spare parts and we introduce a forecasting evaluation method using inventory cost performance curves based on over and under forecast error. We compare, using a well-known use case presented in the literature, the results given by SVM algorithm to those given by several popular forecasting approaches. We find that SVM performs better than, or equivalently to, the other methods for this use-case. We also perform some forecasting experiments using the historical data of forty CAF spare demand series with 84 periods (months) each. The results of the experiments show that SVM may offer forecasting improvements over many other methods however the performance of SVM is not quite as good on intermittent data (time series with a high Average Demand Interval-ADI and low Coefficient of Variation-CV).
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it