Time Series Analysis and Prediction on Bitcoin
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
Bitcoin is the most famous digital currency in the world and has become an investment asset. Prediction is one of the important matters in the investment market. In the economic field, there are different studies on the reasons for the price change of Bitcoin and how to predict the price trend of Bitcoin or how Bitcoin studies the market. Therefore, for Bitcoin, predicting the trend of Bitcoin price can effectively help Bitcoin investors. Data from www. Coingecko, the price of bitcoin is sorted according to the time sequence. Using the time series model, the change of bitcoin price in a specific period which is from 28 April 2013 to 22 August 2022 is calculated to predict the future trend of bitcoin price. Data preprocessing includes attributes removal, stationary test, and differencing. In predicting the price of Bitcoin, the ARIMA method that can produce high accuracy in short-term prediction is adopted. Use prediction test AIC and Check the residuals to select the best prediction model among the candidate models. The results of model testing show that AIC of ARIMA (5,1,2) is the smallest among all candidate models, and the results of residual check also show that ARIMA (5,1,2) model is the best model for predicting four periods.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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