Forecast and Analysis for Stock Market of the U.S, Canada, and Mexico based on Time Series Forecasting Models
Bibliographic record
Abstract
Forecasting the stock market index has been an essential part of the investing process for the world’s investors, so predication for the composite stock market index of three different countries in North America were made for the investors to get references. The weekly data of three representative composite stock market index for each country from the past three months were chosen to generate the prediction for the next week’s performance of the stock market in each country through different time-series forecasting methods. The correlation between each index are calculated, indicating the short-term relationship between each country’s stock market. Three time-series predicting method is produced: SMA, WMA, and SES, one of the three methods with the least error by comparing the MSE and MAD would be selected for each stock index. Analyze the forecast results from the selected method for each country’s composite stock market index and compare them. The forecast results show that the composite stock market index for all three countries is going to decline in the following week. Several short-term relationships between different countries’ stock markets are revealed. The results and the discussion of this research tend to serve as a reference or an indicator for investors who have interests in multiple countries’ stock markets in the world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".