Performance of machine learning methods in predicting trend in price and trading volume of cryptocurrencies
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 study is motivated by the growing interest in cryptocurrency trading and the need for accurate forecasting tools to guide investment decisions. The main aim is to forecast price and trading volume changes of cryptocurrencies by determining their movement directions. Naïve Bayes, support vector machines, logistic regression, regression trees, and the K-nearest neighbors’ algorithm are selected to solve the problem and compared. Performance measures such as accuracy, sensitivity, and specificity are used to assess the models. The study shows that some models are better at predicting volume trends than price trends in cryptocurrencies. Naïve Bayes is good at spotting positive trends, while Logistic Regression is accurate at identifying negative trends. Interestingly, the research reveals that shorter prediction times are more accurate for price forecasts, but intermediate times work better for specificity. These insights help us understand which models work well for different aspects of cryptocurrency 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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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