时间自回归矩阵因子化用于高维时间序列预测OSS的OSS
概括
预测开源软件 (OSS) 的发展至关重要. 一个新的时间自回归矩阵因子化 (TAMF) 框架使用行为数据准确预测OSS趋势,即使有噪音和缺失值.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 数据科学数据科学数据科学
背景情况:
- 开源软件 (OSS) 在现代开发中至关重要.
- 预测OSS未来的发展是必不可少的,但具有挑战性.
- OSS行为数据是带有噪声和缺失值的高维时间序列,需要可扩展的模型.
研究的目的:
- 为预测开源软件开发提出一个可扩展的框架.
- 为了应对OSS行为数据集中杂,高维和不完整的时间序列数据所带来的挑战.
- 为了提高时间序列预测模型的准确性和多功能性,用于OSS开发.
主要方法:
- 开发了一个时间自回归矩阵因子化 (TAMF) 框架.
- 使用趋势和时期自回归模型提取趋势和时期特征.
- 采用基于图形的矩阵分解 (MF) 来通过利用时间序列相关性来归算缺失的值.
- 使用训练后回归模型对目标数据进行最终预测.
主要成果:
- 对于高维时间序列数据,TAMF表现出良好的可扩展性.
- 该框架在来自GitHub的现实世界OSS行为数据上实现了高预测准确度.
- 在不同类型的时间序列数据中,TAMF被证明是多功能性的.
结论:
- 拟议的TAMF框架为预测开源软件开发提供了一个强大的解决方案.
- TAMF有效地处理杂和不完整的高维时间序列数据.
- 该框架的可扩展性和准确性使其适用于OSS开发分析中的实际应用.
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