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About Trend Prediction Classification For High Frequency Bitcoin Time Series With Deep Learning
This article proposes a trend prediction classification framework called the random sampling method (RSM) for analyzing Bitcoin time series data. RSM is based on deep learning and metric learning. It compares input patterns to training samples using a novel sampling scheme to predict price trends. The article finds RSM outperforms baseline methods in mitigating class imbalance issues for unstable Bitcoin prices. However, its profit rates did not exceed a simple buy-and-hold strategy, indicating it does not provide a basis for algorithmic trading within the tested period.
Detailed Information
| Author: | ['alexverde3'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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