Nasdaq and Shanghai Composite Index 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
Accurately predicting asset prices is crucial for investors, as it not only helps them avoid risks and seize market opportunities, but also influences economic trends, optimizes resource allocation, and ensures the scientific and effective nature of investment decisions. In this paper, ARIMA model and ETS model are used to predict the time series of NASDAQ index and Shanghai Composite Index respectively. By comparing the prediction performance indexes of the two models, such as mean square error (MSE) and root mean square error (RMSE), it is found that ARIMA model has higher prediction accuracy in most cases. Besides, the results show that the performance of different models in different markets is different, which provides a basis for investors to choose the right forecasting model. This study provides valuable empirical analysis for the field of asset price forecasting, especially in the context of global economic uncertainty, and has important practical application value.
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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 | 0.000 | 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 | 0.000 | 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