A Study on China's Monthly Temperature Based on the Seasonal Autoregressive Integrated Moving Average
Bibliographic record
Abstract
This study employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to analyze and forecast China’s monthly average temperature data from January 1990 to December 2010, using data from 2011 to 2013 as the test set. The objective is to explore temperature trends and provide a scientific basis for short-term temperature forecasting. Compared with the standard ARIMA model, SARIMA is more effective in modeling data with seasonality. Since the original dataset meets the stationarity requirements, it was directly used for model fitting. The model parameters—including autoregressive, moving average, seasonal autoregressive, and seasonal moving average terms—were automatically selected using R code to ensure accurate fitting of the temperature time series. The results show that the SARIMA model effectively captures both seasonal fluctuations and long-term trends in temperature, yielding reliable short-term forecasts. The final model, , achieves a Mean Absolute Percentage Error (MAPE) of 8.37% on the test set, meeting the expected level of predictive accuracy. The model's forecasting capability offers valuable support for climate policymaking, adaptive strategies to climate change, and sustainable development planning.
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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 | 0.000 |
| 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) | 0.000 | 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".