The Reduced Rules Rule Based Forecasting Decision Support System: Details and Functionalities: An Audit Context
Bibliographic record
Abstract
In execution of PCAOB audits at the Planning and Substantive Phases, forecasts of various financial account balances are often used to collect information on the veracity of the client’s final reported balances. One of the forecast methods widely acclaimed in the academic context is the Rule Based Forecasting [RBF] model of Collopy and Armstrong [C&A]. However, for the most part, the RBF has not found its way into the panoply of the auditor. In our practice-oriented experiential context, the reason for this seems to be the lack of an enabling Decision Support System[DSS] usually needed to create reliable RBF-forecasts in a timely manner needed at the Substantive Phase of the audit. Focus In this report, we detail a GUI-friendly DSS, the VBA-programming of which is based upon a 2013 revision of an updated C&A model offered by Adya and Lusk. The DSS is called: The Reduced Rules: Rule Based Forecasting: Decision Support System [RR:RBF:DSS]. We provide a comprehensive example of the RR:RBF:DSS in a PCAOB-audit context for a Caterpillar™, Inc.Ò account Panel downloaded from Bloomberg™. This example, carefully details all of the numerous User Form-Launch platforms as well as discusses the statistical and operational Rule-scoring functionalities of the RR:RBF:DSS. The RR:RBF:DSS is available as a download without cost or restrictions on its use.
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How this classification was reachedexpand
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".