Commodity futures price forecast based on multi-scale combination model
Bibliographic record
Abstract
Along with developing the commodity futures market, its promoting effect on China’s economic development has gradually increased. Research on the price prediction of commodity futures has important practical significance to society and enterprises. However, commodity futures price series often show nonstationary and nonlinear characteristics In this paper, a new multi-scale combined prediction model is proposed, which combines variational mode decomposition (VMD), long short-term memory neural network (LSTM), and improved self-attention mechanism (XNSA). First, VMD decomposes futures prices into several components to reduce their nonstationarity. Then, the LSTM model with an improved self-attention mechanism (XNSA) is used to model and optimize the decomposed sub-sequences so that the model can concentrate on learning more important data features and further improve the prediction performance. In order to verify the effectiveness of this method, this paper takes No. 1 Soybeans Futures, Corn Futures, and Soybean Meal Futures daily closing price series from Dalian Commodity Exchange as representatives to predict their future return trend. The results show that compared with the existing combination forecasting models, the proposed multi-scale combination model (VMD-LSTM-XNSA) has better forecasting performance. It lays the foundation for developing a corresponding quantitative investment strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".