Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada
Bibliographic record
Abstract
This research aims to compare ARIMA and GRNN models alone. For this comparison the death rate for diabetes mellitus time series data of Canada is used. Autoregressive Integrated Moving Average (ARIMA), and Generalized Regression Neural Networks (GRNN) models were applied for time series prediction of the death rate for diabetes mellitus—trained data for two models from 2000 to 2015. Test data was used to compare the precision through data from 2016 to 2021. The ARIMA model was applied using the auto-command through R package which provided the least BIC and AIC values. The mean absolute deviation (MAD), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were employed to measure the forecasting efficiency of the models. The ARIMA model had the highest prediction accuracy as compared to the GRNN model. ARIMA predicts the death rate for diabetes mellitus more accurately and robustly compared to the GRNNs model.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".