Power Customer Complaint Prediction Model Based on Time Series Analysis
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
To improve customer service of power enterprises, this paper constructs an intelligent prediction model for customer complaints in the near future based on the big data on power service. Firstly, three customer complaint prediction models were established, separately based on autoregressive integrated moving average (ARIMA) time series algorithm, multiple linear regression (MLR) algorithm, and backpropagation neural network (BPNN) algorithm. The predicted values of the three models were compared with the real values. Through the comparison, the BPNN model was found to achieve the best predictive effect. To help the BPNN avoid local minimum, the genetic algorithm (GA) was introduced to optimize the BPNN model. Finally, several experiments were conducted to verify the effect of the optimized model. The results show that the relative error of the optimized model was less than 40% in most cases. The proposed model can greatly improve the customer service of power enterprises.
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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 | 0.000 | 0.001 |
| 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) | 0.025 | 0.008 |
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