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 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 | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".