Deep Learning Ensemble Based Model for Time Series Forecasting Across Multiple Applications
Bibliographic record
Abstract
Time series prediction has been challenging topic in several application domains. In this paper, an ensemble of two top performing deep learning architectures across different applications such as fresh produce (FP) yield prediction, FP price prediction and crude oil price prediction is proposed. First, the input data is trained on an array of different machine learning architectures, the top two performers are then combined using a stacking ensemble. The top two performers across the three tested applications are found to be Attention CNN-LSTM (AC-LSTM) and Attention ConvLSTM (ACV-LSTM). Different ensemble techniques, mean prediction, Linear Regression (LR) and Support vector Regression (SVR), are then utilized to come up with the best prediction. An aggregated measure that combines the results of mean absolute error (MAE), mean squared error (MSE) and R2coefficient of determination (R2) is used to evaluate model performance. The experiment results show that across the various examined applications, the proposed model which is a stacking ensemble of the AC-LSTM and ACV-LSTM using a linear SVR is the best performing based on the aggregated measure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 source (direct Gemma or distilled Codex), 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".