Enhancing Export Management Strategies: A Comparative Analysis of Forecasting Models for Lemongrass Oil
Bibliographic record
Abstract
The proposed study focuses on evaluating the forecasting behavior of lemongrass oil export value (in lakh Indian Rupees) from India. To explore the data series, descriptive statistical measures such as central tendency, dispersion, skewness, and kurtosis were computed. Based on the study objectives, two forecasting techniques-TBATS (a hybrid model incorporating Exponential Smoothing, Box-Cox Transformation, ARMA errors, and Trigonometric seasonal components) and Holt's Linear Trend method were applied and compared. The selection of the optimal model was guided by goodness-of-fit metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE), evaluated across both training and testing datasets. Model adequacy was further assessed using the Ljung-Box (LB) test on residuals. The TBATS model emerged as the best fit for forecasting export data to France, Germany, Spain, Canada, the People's Republic of China (PRC), Singapore, and India. In contrast, Holt's linear trend model was found optimal for the United States (USA), the United Kingdom (UK), Australia, Thailand, and an aggregated category representing the top ten importing countries and other remaining nations. This model selection was based on achieving lower error values in goodnessof- fit measures. The results underscore the importance of selecting appropriate statistical models tailored to country-specific export patterns and provide policymakers and stakeholders with a robust framework for decision-making in the context of managing the volatile lemongrass oil export market.. KEYWORDS :TBATS, Holt's linear trend, Forecasting, Time series, Export value of lemongrass oil.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".