Hybrid Time‐Series Prediction Method Based on Entropy Fusion Feature
Why this work is in the frame
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Bibliographic record
Abstract
High‐precision time sequence forecasting is a complicated cyber‐physical system (CPS) task. Due to the diversity of data scales and types, the classic time‐series prediction model meets the challenge to deliver accurate prediction results for many forms of time‐series data. This work proposes a hybrid model with long short‐term memory (LSTM) and embedded empirical mode decomposition (EEMD) based on the entropy fusion feature. First, we apply EEMD in entropy fusion feature long short‐term memory (ELSTM) to lessen pattern confusion and edge effects in traditional empirical mode decomposition (EMD). The sequence is then divided into intrinsic mode functions (IMF) by using EEMD. Then, feature vectors are constructed between IMFs and their respective information entropy for feature merging. LSTM is used to build a full connection network for each entropy fusion feature IMF subsequence for prediction and each type of IMF subsequence as the feature dimension to obtain its prediction results. Finally, the output results of all IMF subsequences are reconstructed to obtain the final prediction result. Compared with the LSTM method, the performance of the proposed method has been improved 64.33% on the evaluation metric MAPE. The proposed model has also delivered the best prediction outcomes across four different time‐series datasets. The experimental results conclusively show that the proposed method outperforms other models compared.
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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 | 0.001 | 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) | 0.000 | 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