RESEARCHING THE FUTURE OF BITCOIN MARKET WITH MACHINE LEARNING METHOD: ANAPPLICATION ON THE CASE OF TURKEY
Bibliographic record
Abstract
It is seen that money takes different forms in line with the changing needs and technological developments throughout the historical process. Recently, cryptocurrencies have been included in our lives with Bitcoin. Bitcoin is a digital currency that functions using cryptographic techniques and without the need for the control of a central authority. As a result of technological developments, it is seen that the interest in Bitcoin, which has entered our lives as a new monetary tool and is predicted to be an alternative to currencies, is increasing. In this article, the machine learning method, which is a branch of artificial intelligence, is used as a method. In the example of Turkey, the daily closing data of bitcoin for 2016 were used. The machine learning method is aimed to predict the closing prices of the bitcoin market.According to the analysis findings, it is seen that the closing prices realized in the 4th quarter are higher than the closing prices realized in the 1st quarter. If the volume USD is higher than 5517.34 then it is Q1. Some rules have been produced with the Machine Learning method.It is aimed to contribute to the literature by using themachine learning method for predicting Bitcoin closing prices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".