Predicting Temperature Anomalies Due to Climate Change Using Time Series Analysis
Bibliographic record
Abstract
Climatic conditions are changing due to climate change which is perceived as the world's most important threat that is currently present. A major condition for the development of adaptive policies and mitigation of negative effects is the predictable occurrence of climatic anomalies. This research work proposed a credible means for forecasting temperature variations due to climate change by the use of time series analysis that takes advantage of historical temperature records and powerful machine learning algorithms. The proposed method heavily depends on a mix of standard statistical techniques, including ARIMA and exponential smoothing, and deep learning architectures like Long Short-Term Memory (LSTM), taking into consideration not just the brief fluctuations but also the great trends of long-term development. The study also adds to the predictive performance climate-related covariates such as the amount of greenhouse gases and solar activity, i.e., the activity of the sun, to increase the accuracy of the forecasts. Selected experiments conducted on publicly available climate databases compared to the straightforward models proved beyond doubt superior results of all forecasting models. In this research you will get the proper knowledge of the nature of the climate-induced temperature anomalies and the base for future works where such complications as the global climate change are addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".