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首页> 外文会议>International Conference on Artificial Neural Networks >DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting
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DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting

机译：DeepEX：弥合时间序列预测的知识和数据驱动技术之间的差距

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摘要

Artificial Intelligence (AI) can roughly be categorized into two streams, knowledge driven and data driven both of which have their own advantages. Incorporating knowledge into Deep Neural Networks (DNN), that are purely data driven, can potentially improve the overall performance of the system. This paper presents such a fusion scheme, DeepEX, that combines these seemingly parallel streams of AI, for multi-step time-series forecasting problems. DeepEX achieves this in a way that merges best of both worlds along with a reduction in the amount of data required to train these models. This direction has been explored in the past for single step forecasting by opting for a residual learning scheme. We analyze the shortcomings of this simple residual learning scheme and enable DeepEX to not only avoid these shortcomings but also scale to multi-step prediction problems. DeepEX is tested on two commonly used time series forecasting datasets, CIF2016 and NN5, where it achieves competitive results even when trained on a reduced set of training examples. Incorporating external knowledge to reduce network's reliance on large amount of accurately labeled data will prove to be extremely effective in training of neural networks for real-world applications where the dataset sizes are small and labeling is expensive.
机译：人工智能（AI）可以大致分为两个流，知识驱动和数据驱动这两个流都有各自的优势。将知识整合到纯粹由数据驱动的深度神经网络（DNN）中，可以潜在地改善系统的整体性能。本文提出了一种融合方案DeepEX，该方案结合了这些看似并行的AI流，从而解决了多步时间序列预测问题。 DeepEX通过融合两个方面的优势并减少训练这些模型所需的数据量来实现这一目标。过去，通过选择残差学习方案，已经为单步预测探索了这个方向。我们分析了这种简单的残差学习方案的缺点，并使DeepEX不仅可以避免这些缺点，而且可以扩展到多步预测问题。 DeepEX在两个常用的时间序列预测数据集CIF2016和NN5上进行了测试，即使在经过简化的一组训练示例中进行训练，也能获得有竞争力的结果。结合外部知识来减少网络对大量经过准确标记的数据的依赖，将证明在针对数据集大小较小且标记昂贵的实际应用中训练神经网络方面极为有效。

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