What does this research mean for the field?

The ARIMA(1,0,1) time-series forecasting model explains 85% of the variability in process-control system efficiency metrics in Senegal. Novelty: ClaimNovelty.CONFIRMATORY. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The central aim is to evaluate the efficiency of process-control systems in Senegal using time-series forecasting models.

March 2, 2026Open Access

Time-Series Forecasting Model Evaluation of Process-Control Systems in Senegal,

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Authors

SSSamba SowUniversité Alioune Diop de Bambey MDMamoudou DiopAfrican Institute for Mathematical Sciences TNToure NdiayeInstitut Sénégalais de Recherches Agricoles

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Implication

Evaluates efficiency improvements in process-control systems, suggesting refined forecasting techniques for better outcomes.

Key Points

The central aim is to evaluate the efficiency of process-control systems in Senegal using time-series forecasting models.
Applied time-series forecasting models to annual production data.
Employed the Box-Jenkins ARIMA model to analyze trends and seasonality.
Utilized ARIMA(1,0,1) for predicting outcomes with high accuracy.
Calculated R² value of 0.85 to explain variability in data.
Checked model robustness using heteroskedasticity-consistent errors.
Achieved an R² value of 0.85, indicating strong model performance.
Determined a margin of error of ±3% for the forecasts.
Recommended further research and more detailed data for improved accuracy.

Cite This Study

Sow et al. (2005) studied this question.

synapsesocial.com/papers/69a52e45f1e85e5c73bf1c71 https://doi.org/10.5281/zenodo.18819328

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Time-Series Forecasting Model Evaluation of Process-Control Systems in Senegal,

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Member takes

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Key Points

Cite This Study

Time-Series Forecasting Model Evaluation of Process-Control Systems in Senegal,

Discussion

Member takes

Implication

Key Points

Cite This Study

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