Research on the Construction and Application of Macroeconomic Forecasting Model Based on Time Series Cluster Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Aiming at the problem of large prediction error caused by the complex background of macroeconomic prediction, this paper proposes a macroeconomic prediction model based on time series clustering.The model adopts sparse self-encoder to deeply mine the features of the input vectors, constructs a bidirectional threshold cyclic unit network, and predicts the preliminary trend of the macroeconomy, and proposes a time series deep clustering algorithm that integrates the multi-scale feature extraction and clustering objectives of time series data into the same network.A sample generation strategy based on data augmentation and a multiclassification assistance module are used to extract the invariant patterns contained in the time series data to obtain a better representation for targeting time series clustering.Comparing this paper's model with different forecasting models, the RMSE metrics are 0.0038 and 0.003 for the two time horizons, which are better than the other two models.The prediction range of this paper's model for future GDP is 5.8%-5.9%,which is smaller than the GDP prediction range of the ARIMA model, indicating that this paper's model is suitable for the realistic application of macroeconomic forecasting.
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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 | 0.013 | 0.002 |
| 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 | 0.000 | 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