Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price
Bibliographic record
Abstract
This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach that links a dual long-memory process (Gegenbauer autoregressive moving average (GARMA) and generalized long-memory GARCH (G-GARCH)) and the empirical wavelet transform (EWT) and local linear wavelet neural network (LLWNN) approaches, forming the k-factor GARMA-EWLLWNN model. The future hybrid model accomplished is assessed via data from the Polish electricity markets, and it is matched with the generalized long-memory k-factor GARMA-G-GARCH process and the hybrid EWLLWNN, to demonstrate the robustness of our approach. The obtained outcomes show that the suggested model presents important results to define the relevance of the modeling approach that offers a remarkable framework to reproduce the inherent characteristics of the electricity prices. Finally, it is presented that the adopted methodology is the most appropriate one for prediction as it realizes a better prediction performance and may be an answer for forecasting electricity prices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".