Forecast of consumption of natural gas in U.S. based on time series analysis and ARIMA model
Bibliographic record
Abstract
Natural gas is a widely used fossil fuel, playing a vital role in both industrial and economic sectors. As a widely used fossil fuel, the prediction of natural gas consumption is crucial for industrial and economics and hence many scholars have been researched on the topic. Due to the weather variation and the sensitivity of industrial demand, a precise consumption forecast is difficult. In the article, ARIMA model, a widely used model in time series analysis and forecasts would be considered in the prediction for consumption for future one year. It turns out that, in the next year, the consumption of natural gas in the coming year is stable and will stay at a still level and the overall trend of the series is increasing, with no significant short-term fluctuations. Therefore, the article suggested that long-term investment in industries related to natural gas is recommended. Besides, short-term investment is not suggested compared to the long-term due to the still level of the consumption within one year.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".