Forecasting Buffer Stock in the Fisheries Industry Using ARIMA and Fuzzy Time Series Markov-Chain Methods
Bibliographic record
Abstract
The fisheries sector is vital for food security but remains vulnerable to supply fluctuations and uncertain stock availability.This study develops a forecasting framework for buffer stock estimation by applying the Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series Markov-Chain (FTS-MC) approaches to historical data from Makassar City, Indonesia during 2021-2025.The ARIMA (2,1,0) model produced acceptable accuracy with a Mean Absolute Percentage Error (MAPE) of 13.67%, whereas the FTS-MC method delivered superior outcomes with reduced errors (MAPE 10.91% and RMSE 246.94).These findings confirm the capability of FTS-MC in addressing volatility and uncertainty, offering more dependable projections of raw material reserves.The study provides practical implications for enhancing fisheries governance, stabilizing market distribution, and supporting strategic planning.Future research should incorporate broader datasets, real-time observations, and environmental parameters to refine predictive performance across varied contexts.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".