Enhancing Predictive Accuracy through the Analysis of Banking Time Series: A Case Study from the Amman Stock Exchange
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
This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE).
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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