Neural Network Forecasting of the Production Level of Chinese Construction Industry
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
Increased efforts have been devoted over the past several decades to the development and improvement of time series forecasting models. In this paper, we determine whether the forecasting performance of variables under study can be improved using neural network models. Among the best 10 retained networks, an MLP 3- layer network: 1:1-31-1:1 is selected as the ANN model with the minimum RMSE. The performance of the model is evaluated by comparing it with the ARIMA model. The root mean squared forecast error of the best neural network model is 49 per cent lower than the ARIMA model counterpart. It shows that the neural network yields significant forecast improvements. The gains in forecast accuracy seem to originate from the ability of neural networks to capture asymmetric relationships. This methodology has been applied to forecast the Chinese construction industry (CI). Since CI contributes to GDP considerably, it has an important and supportive role in the national economy of China. The empirical results show that the trend of steadily increasing production levels of CI implies a strong potential for future growth.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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