Time-Series Forecasting of Cryptocurrency Prices Using High-Dimensional Features and a Hybrid Approach
Bibliographic record
Abstract
Nowadays, digital cryptocurrencies are the most popular asset, especially for international exchanges.Bitcoin is the earliest cryptocurrency that succeeded in being used in financial transactions.Bitcoin stores the transactions in Blockchain technology.Bitcoin price has been unstable during the time from 0.5$ to about 60,000$ since 2010.Many efforts exist to predict Bitcoin value or its fluctuations using machine learning techniques.The price prediction is usually more challenging than fluctuations prediction, and its performance metrics are improved.This study introduces a methodology to predict Bitcoin price in a dataset, including four intervals to evaluate the proposed method in different situations.The experimental results show that the generalized linear model and Long Short-Term Memory (LSTM) were the best machine learning techniques.The proposed model outperforms the deep learning baseline model with about 18% and 20% relative improvement in mean absolute error and means absolute percentage error, respectively.Deep learning approaches have achieved much better results than other approaches due to the automatic selection of features.Compared to the results reported in the literature, the 1D-CNN+IndRNN proposed approach has reached 81% accuracy, with an 18% improvement.In the proposed approach, 1D-CNN is responsible for feature extraction and IndRNN is responsible for learning features in the form of time series.Appreciating to many people, from all over the world, who so generously helped me during my master's degree and contributed to this thesis work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".