Prediction of World Crude Oil Price with the Method of Missing Data
Bibliographic record
Abstract
As the fluctuation of oil price plays an important role in global political and economic situation, forecasting the price of oil is significant. In this paper, we analyze the data of the world crude oil price using ideas of treating with the missing data, i.e. we take the predictor as missing data and use the EM algorithm to establish time series model. We give the predictive values of weekly world crude oil price of January and February in 2011 using the data of 2009 and 2010. Meanwhile, we found that the method based on missing data is more effective than normal time series method by comparing the predictive value with reality data. In addition, this method is also applicable to the case that historical observations have missing data. Key words: World Crude Oil Price; Forecast; Missing Data; EM Algorithm; Time Series
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.017 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".