Forecasting of Immigrants in Canada using Forecasting models
Bibliographic record
Abstract
In Canada, the number of international students, temporary workers and refugees from every part of the world grows each year. Therefore, forecasting immigration is important for the economy of Canada and Labor Market. In this regard, four forecasting approaches have been applied to the annual data of immigrants for the period 2000-2019. The accuracy of Moving average (MA), Autoregressive (AR), Autoregressive moving average (ARMA), Autoregressive integrated moving average (ARIMA) models were checked via comparing Akaike’s information criteria(AIC), Bayesian information criteria (BIC), Mean error (ME), Root mean square error(RMSE), Mean absolute error (MAE), Mean percentage error (MPE), Mean absolute percentage error (MAPE) and Mean absolute scaled error (MASE) and graphical approaches such as ACF plots of residuals. Experimental results showed that ARIMA (1,2,4) is the best-fitted model for forecasting immigrants in Canada. Selected forecasting approaches are applied to predict immigrants for five years from 2020-2024.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".