Evaluation of Imputation Models Based on the Enhancement to Yield Forecasting
Bibliographic record
Abstract
Market price and yield forecasting models for Fresh Produce (FP) are crucial to protect retailers and consumers from overpriced FP. However, utilizing the data for forecasting is obstructed by the occurrence of missing values. Therefore, it is imperative to impute the encountered missing instances to enable effective forecasting. Most of the work found in literature tackles imputation of missing values when they are randomly scattered in the dataset while very little work is found tackling both: consecutive occurrence of missing data, i.e. missing data chunks, as well as those randomly missing. In this work, the data used for forecasting has missing values in chunks as well as at random points. Therefore, various comprehensive imputation models are used to impute both random as well as chunks of missing values. Since the imputed time series are incomplete, the only way to evaluate those imputation models is to analyze their effect on forecasting performance. The ensemble of two compound deep learning (DL) models, namely Attention Convolutional Neural Networks Long Short Term Memory (Att-CNN-LSTM) and SeriesNet with Gated Recurrent Unit (GRU), is used for forecasting. For imputation, three DL models are tested: The Ensemble imputation model which is a Voting Regressor of two DL submodels, Residual GRU and LSTM-Deep-GRU. Another deep learning imputation model is used which is a Transfer Learning (TL) model. Finally, a Hybrid model of both DL models is designed to take the pros of each of its integrated models by using the Ensemble model in case of random missing data and the Transfer Learning model in case of missing data chunks. It is observed that, in general, imputing the missing values improves the forecasting result as compared to eliminating the instances with missing values. The Hybrid model improves the overall forecasting performance by up to 60% compared to the case of using the second-best Transfer Learning model and around 64% as compared to the case of imputation using the Ensemble model.
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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 | 0.004 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".