Deep Learning Based Approach for Fresh Produce Market Price Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Building highly precise prediction models for Fresh Produce (FP) market price is crucial to protect retailers from overpriced FP. In this paper we are comparing the price prediction models performance of deep learning (DL) models with statistical as well as standard machine learning (ML) models. Five types of FP are considered in performance testing. It is found that the conventional ML models outperform the statistical models such as ARIMA. On the other hand, the winning model among the conventional ML models (the Gradient Boosting model) proves to be less performant as compared with the simple or compound DL models. Moreover, the simple DL models, such as the Long Short-Term Memory (LSTM), are outperformed by the compound one, the Convolutional Long Short-Term Memory Recurrent Neural Network (CNN-LSTM), whose performance improves by adding attention. The model is capable of precisely predicting FP prices for up to three weeks ahead.
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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 | 0.009 | 0.069 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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