SSEC Forecast Based on ARIMA and ETS Models
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
Stock price forecasting is the main concern of the financial industry, which is affected by many macroeconomic variables. The Shanghai Securities Composite Index (SSEC), as a representative index of the Chinese stock market, provides an overall overview of the performance of the Chinese capital market. China's economic policy changes, especially the adjustment of monetary policy, have had a significant impact on the stock market. In this paper, ARIMA and ETS models are used to forecast SSEC under the current macroeconomic environment. The results show that the root mean square error (RMSE) value of ETS model is lower than that of ARIMA model, indicating that ETS model is more accurate in SSEC prediction. In addition, the ETS model is particularly suitable for stock market forecasting due to its ability to account for exponential trends. In order to provide a new perspective for predicting the Shanghai Stock Composite Index and provide guidance for stock market forecasting under complex economic environment, the ARIMA and ETS models are analyzed in this study.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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