Predicting the Number of Sunspots Per Month and Per Quarter Using ARIMA Models
Bibliographic record
Abstract
The number of sunspots in a given year changes as the sun goes through solar cycles, with peaks happening at regular intervals.When these peaks are plotted, a curve appears, similar to the oscillating sinusoidal wave.Because of its oscillatory nature, predictions of future sunspot values could be found since it is safe to assume that the number of sunspots would always follow a pattern.However, a simple, ordinary sine function, or any algebraic function for that matter, would not allow us to plot and predict future data points due to the complexity of the curve at hand.This led us to the hypothesis that in order to predict the future number of sunspots, models that involve autoregressive and moving average components (namely the ARIMA model) would be the most effective.In order to measure effectiveness, the mean-squared error (MSE) would be used, with a lower value (closer to 0) meaning better fit.The reason why we chose these sophisticated models was because these models took into account prior data points and their trends and seasonality to predict future data points.This essentially meant that this model would predict based on prior points, not on a fixed point or equation, like the sine curve.After plotting all of these models and finding the MSE for each, we drew the conclusion that the ARIMA model proved to produce the most accurate curve, with a MSE of only 460, as compared to the MSE that the best sine curve could produce: 21 million.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".