Comparative analysis of automatic time-series forecasting approaches for potato wholesale price index in India
Bibliographic record
Abstract
This paper investigates the effectiveness of 11 automatic time-series forecasting techniques in forecasting the wholesale price index (WPI) of potatoes in India. Techniques include autoregressive integrated moving average (ARIMA), error-trend-seasonality (ETS), four artificial neural network (ANN) models, and five hybrid approaches. Evaluation is based on mean absolute percentage error (MAPE). The forecast horizon extends up to 15 months. This work revealed that the ETS-ANN method is the most effective, showcasing an average MAPE of 5.42%. The improvement of the forecast accuracy of the hybrid ETS-ANN over the naive (baseline) is 59.8%, ETS is 29.18%, and ANN is 41.85%. It indicates a significant enhancement in forecast accuracy. The ETS-ANN approach exhibited statistically significant results. It validates the ETS-ANN technique's effectiveness in accurately forecasting the potato WPI in India. It contributes to this specific domain and provides valuable insights for policymakers and stakeholders. Additionally, it may serve as a methodological guide for other agricultural commodities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".