Supervised learning models, statistical models or hybrid models? A prediction of clean energy stock based on fear and fundamental factors
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
This paper explores several time series models for predicting the S&P Clean Energy Index. We begin by identifying factors previously found to influence the clean energy market and use eXtreme Gradient Boosting (XGBoost) to rank and filter feature importance of variables, followed by further validation and variable selection using SHAP values. Next, we simulate future feature data using methods like Random Forest and Long Short-Term Memory (LSTM). For the LSTM-based simulations, the data is generated through a classification-then-prediction approach using the K-Nearest Neighbors (KNN) algorithm. To predict the index’s volatility, we employ statistical models such as AutoRegressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR). Additionally, we use advanced methods like LSTM, Supervised Autoencoder (SAE), and hybrid models such as Prophet Features and LSTM_Autoregressive(LSTM_AR). Each model’s parameter-tuning process will be explained in detail. Finally, we compared the models’ performance and prediction results, discussing their strengths and suitability for different scenarios. We confirmed that the Prophet-based models performed well on Random Forest simulated data when predicting both the trend and actual values of the S&P clean energy index.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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