Modelling and Forecasting U.S. and Canada Unemployment Rates with SARIMA and BSTS
Bibliographic record
Abstract
Unemployment has taken on more research significance nowadays when the job market is becoming increasingly depressed. Understanding the causes and patterns of unemployment rates can not only help the public formulate strategies to address the inactive job market, but also develop practical and feasible policies to restore the economy. To investigate the nature of unemployment rates and to find which tool fits better for forecasting them, this article compares two time series analysis models (SARIMA and BSTS) by using several univariate regional datasets from the U.S. and Canada to assess the model performances. Overall, the unemployment rates for the U.S. are more volatile than those for Canada. Through constructing various time series models and providing forecasting results on periods with different lengths, the results show that BSTS generally tends to capture the overall trend of unemployment rates better, whereas SARIMA fails to show the right trend when a longer forecasting period is applied. The result is perhaps because BSTS has a more well-rounded algorithm in updating statistical assumptions, and hence, BSTS might handle irregular data like unemployment rates better than SARIMA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".