Time Series Modeling and Forecasting Using Autoregressive Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time series analysis is pivotal in discerning temporospatial data patterns and facilitating precise forecasts.This study scrutinizes the cardinal challenges associated with time series modeling, namely stationarity, parsimony, and overfitting, focusing on the application of Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models.An examination of six datasets reveals that these models adeptly encapsulate underlying data trends, enabling reliable predictions and yielding insightful conclusions.Relative to baseline methods, the proposed models demonstrate superior performance, as indicated by five evaluation metrics: Mean Squared Error (MSE), Frantic, Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Theil's U-statistics.The most parsimonious ARIMA or SARIMA model was selected for each dataset, with the resultant forecast summary graphically demonstrating the proximity between original and predicted observations.This study aims to contribute to the discourse on the validity and applicability of ARIMA and SARIMA models in time series analysis and forecasting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it