Development of SARIMA Model to Predict Quarterly Apparel and Textile Export Revenue in Sri Lanka
Bibliographic record
Abstract
Apparel and textile exports play a significant role in the Sri Lankan economy. The USA, UK, Italy, Germany, and Belgium are the main markets of apparel and textile exports in Sri Lanka. Advanced knowledge of export revenue is vital important for various reasons. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model of the type (1,1,0) x (0,1,1)4 was developed to model apparel and textile export revenue in Sri Lanka using quarterly data from year 2004 quarter 1 (2004Q1 ) to year 2021 quarter 4 (2021Q4 ). The errors of the model were found to be random and have a constant variance. The best fitted model was identified by comparing various statistical indicators, namely, the Akaike info criterion, Schwarz criterion, Hannan-Quinn criterion, Log likelihood criterion and volatility of six possible models decided based on sample ACF and PACF of the stationary series. The model was validated for data from year 2022Q1 to 2023Q1 . The Mean Absolute Percentage Error (MAPE) for the training data set and validation data set were 7.68% and 11.35% respectively. The predicted revenues (Mn USD) for the 2023Q2 to 2024Q4 are 1074.23, 1263.30, 1222.22, 1206.74, 1058.38, 1265.00 and 1216.58, respectively. The forecasted values for short-term periods can be effectively used by the decision makers for various activities. The model developed is easy to use and reliable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".