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Graph Structure Learning-Based Neural Network for ETF Price Movement Prediction
Hyeonsoo Jo, Jin-gee Kim, Taehun Kim, Kijung Shin
http://doi.org/10.5626/JOK.2024.51.5.473
Exchange-Traded Funds (ETFs) are index funds that mirror particular market indices, usually associated with their low risk and expense ratio to individual investors. Various methods have emerged for accurately predicting ETF price movements, and recently, AI-based technologies have been developed. One representative method involves using time-series-based neural networks to predict the price movement of ETFs. This approach effectively incorporates past price information of ETFs, allowing the prediction of their movement. However, it has a limitation as it only utilizes historical information of individual ETFs and does not account for the relationships and interactions between different ETFs. To address this issue, we propose a model that can capture relationships between ETFs. The proposed model uses graph structure learning to infer a graph representing relationships between ETFs. Based on this, a graph neural network predicts the ETF price movement. The proposed model demonstrates superior performance compared to time-series-based deep-learning models that only use individual ETF information.
Recursive Compaction Method of LSM-tree based Key-value Store
Jongbin Kim, Seohui Son, Hyunsoo Cho, Hyungsoo Jung
http://doi.org/10.5626/JOK.2019.46.9.946
LSM-tree-based key-value stores exhibit an optimized structure for data writing operations and typically maintain the form of LSM tree by executing a compaction operation. The compaction operation which reads data from the storage device into memory for sorting it and writes back the result data in to the storage device several times causes some problems. In this paper, we analyzed the performance degradation and the write amplification caused by the compaction, and proposed a new compaction method known as recursive compaction. Recursive compaction alleviates the problems involving the compaction operation by utilizing multiple threads to perform multiple compactions at a time, handling read operation and garbage collection properly. We implemented this technique for Google LevelDB and analyzed the results.
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