Forecasting with Univariate Time Series Models: A Case of Export Demand for Peninsular Malaysia’s Moulding and Chipboard
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Bibliographic record
Abstract
This study determines a suitable method from the univariate time series models to forecast the export demand of moulding and chipboard volume (m³) from Peninsular Malaysia using the quarterly data from March 1982 to June 2009. Export demand for moulding and chipboard were estimated using univariate time series models including the Holt-Winters Seasonal, ARAR algorithms and the seasonal ARIMA models. The seasonal ARIMA (1, 0, 4) X (0, 0, 1, 0)4 model produced the best forecast at the lowest forecast errors of MAPE, MAE and RMSE at 18.83%, 32730.8 and 35282.13, respectively. It forecasts the volume (m³) of moulding and chipboard for export to reach more than 150000 m3, and it is expected to be within range of 100000 to 250000 m3 at 95% confidence level. The forecasts assist in decision making process and facilitate a short-term marketing plan to meet the export demand from international market.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it