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MLP, Gaussian Processes and Negative Correlation Learning for Time Series Prediction

机译:MLP,高斯过程和负相关学习用于时间序列预测

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摘要

Time series forecasting is a challenging problem, that has a wide variety of application domains such as in engineering, environment, finance and others. When confronted with a time series forecasting application, typically a number of different forecasting models are tested and the best one is considered. Alternatively, instead of choosing the single best method, a wiser action could be to choose a group of the best models and then to combine their forecasts. In this study we propose a combined model consisting of Multi-layer perceptron (MLP), Gaussian Processes Regression (GPR) and a Negative Correlation Learning (NCL) model. The MLP and the GPR were the top performers in a previous large scale comparative study. On the other hand, NCL suggests an alternative way for building accurate and diverse ensembles. No studies have reported on the performance of the NCL in time series prediction. In this work we test the efficiency of NCL in predicting time series data. Results on two real data sets show that the NCL is a good candidate model for forecasting time series. In addition, the study also shows that the combined MLP/GPR/NCL model outperforms all models under consideration.
机译:时间序列预测是一个具有挑战性的问题,它具有广泛的应用领域,例如在工程,环境,金融等领域。当面对时间序列预测应用程序时,通常会测试许多不同的预测模型并考虑最佳模型。或者,代替选择单一的最佳方法,更明智的选择是选择一组最佳模型,然后组合其预测。在这项研究中,我们提出了一个包含多层感知器(MLP),高斯过程回归(GPR)和负相关学习(NCL)模型的组合模型。在先前的大规模比较研究中,MLP和GPR是表现最好的。另一方面,NCL提出了另一种构建准确多样的乐团的方法。尚无研究报道NCL在时间序列预测中的性能。在这项工作中,我们测试了NCL在预测时间序列数据中的效率。在两个真实数据集上的结果表明,NCL是预测时间序列的良好候选模型。此外,研究还表明,组合的MLP / GPR / NCL模型优于所考虑的所有模型。

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