Forecasting models for Quebec’s lumber demand and exports using multivariate regression technique
Bibliographic record
Abstract
The business environment of the forest products industry is impacted by a variety of factors that makes it hard to predict the market’s behavior. Moreover, companies operating in this industry are continuously seeking to improve their understanding of the market by transforming available data into valuable knowledge and meaningful forecasts. This paper proposes a methodology to extract and use open data for Quebec’s lumber demand and exports forecasts using multivariate regression techniques. A number of methods were applied to estimate the models’ coefficients using a training data set, namely the Ordinary Least Squares method with a “backward” variable selection approach, LASSO and RIDGE regressions, and the Two-Step Least Squares method. Then their forecast accuracy was tested on an out-of-sample data set. The best selected models in terms of forecast accuracy succeeded in predicting Quebec lumber demand and exports on the testing data set, with a Root Mean Square Error of 0.12 and 0.08 respectively, and a Mean Absolute Error of 0.1 and 0.06 respectively. Furthermore, the developed data visualization tool appeared as a powerful tool to highlight the reliable forecasts generated by the models, while deducing relevant information through interactive graphics. Such a visualization tool could therefore help in better understanding the market when making decisions related to the evolution of lumber demand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".