Multi‐Step Forecasting of U.S. Maritime Transportation Flows Using Hybrid ARIMA, PCR, CNN, and Recurrent Neural Network Models
Bibliographic record
Abstract
Maritime transportation plays an essential role in the global economy, supporting trade, supply chains and the development of countries. Forecasting maritime traffic and shipping connectivity between different ports in the United States can help us optimize trading strategy, improve global economic competition and promote technological innovation,. In this paper, six time series forecasting models, ARIMA, PCR, GRU, CNN, RNN and LSTM, are used to forecast the mode of maritime transportation trade in United State and compare between each model to discuss which fits well and further apply to future three years. This approach enables a comparative analysis of the relationship between port performance and geographic time-zone divisions. Applying data from The United Nations Conference on Trade and Development (UNCTAD) to the models demonstrates that the most suitable model of the data is PCR. With accurate forecasts, policymakers and stakeholders could formulate forward-looking strategies for risk mitigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".