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Time series prediction using adaptive association rules

机译：使用自适应关联规则的时间序列预测

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Grid computing is formed by a large collection of interconnected heterogeneous and distributed system. One of the grid computing purposes is to share computational resources. The efficiency and effectiveness of resource utilization of a grid greatly depend on the scheduler algorithm. The scheduler is able to manage the grid resources more effectively if we able to predict and provide it with the future state of grid resources. Therefore, this paper proposes a model to perform time series prediction using adaptive association rules. This model uses the idea that if a segment of a repeatable time series pattern has occurred, it has the possibility that the following segments of the repeatable pattern appear. Data mining and pattern matching techniques are being applied to mine for repeatable time series patterns. This model has the ability to provide confident level for each prediction it made and perform continuous adaptation. A prototype of this model is being developed and tested with four test cases. These test cases are relatively simple because our work on this time series prediction using adaptive association rules is very much in its early stages. The result from the experiment shows that our model is able to capture repetitive time series patterns and perform prediction using those patterns. However, this model has some drawbacks such as it required high computational power and required large storage.
机译：网格计算是由大量互连的异构和分布式系统组成的。网格计算的目的之一是共享计算资源。网格资源利用的效率和有效性在很大程度上取决于调度程序算法。如果我们能够预测并提供网格资源的未来状态，则调度程序能够更有效地管理网格资源。因此，本文提出了一种使用自适应关联规则进行时间序列预测的模型。该模型使用的想法是，如果出现了可重复时间序列模式的一部分，则可能出现以下可重复模式的片段。数据挖掘和模式匹配技术正被用于挖掘可重复的时间序列模式。该模型具有为所做的每个预测提供置信度并执行连续调整的能力。该模型的原型正在开发中，并使用四个测试用例进行了测试。这些测试案例相对简单，因为我们使用自适应关联规则进行时间序列预测的工作还处于早期阶段。实验结果表明，我们的模型能够捕获重复的时间序列模式并使用这些模式执行预测。但是，该模型具有一些缺点，例如需要高计算能力和大容量存储。

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