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Automatic specification of piecewise linear additive models: application to forecasting natural gas demand

机译：分段线性添加剂模型的自动规范：在天然气需求预测中的应用

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摘要

When facing any forecasting problem not only is accuracy on the predictions sought. Also, useful information about the underlying physics of the process and about the relevance of the forecasting variables is very much appreciated. In this paper, it is presented an automatic specification procedure for models that are based on additivity assumptions and piecewise linear regression. This procedure allows the analyst to gain insight about the problem by examining the automatically selected model, thus easily checking the validity of the forecast. Monte Carlo simulations have been run to ensure that the model selection procedure behaves correctly under weakly dependent data. Moreover, comparison over other well-known methodologies has been done to evaluate its accuracy performance, both in simulated data and in the context of short-term natural gas demand forecasting. Empirical results show that the accuracy of the proposed model is competitive against more complex methods such as neural networks.
机译：当面对任何预测问题时，不仅要寻求预测的准确性。此外，非常感谢有关过程的基本物理原理和预测变量的相关性的有用信息。在本文中，提出了一种基于加性假设和分段线性回归的模型自动指定程序。此过程使分析人员可以通过检查自动选择的模型来了解问题，从而轻松地检查预测的有效性。进行了蒙特卡洛模拟，以确保模型选择过程在弱相关数据下正确运行。此外，已经在模拟数据和短期天然气需求预测的背景下与其他众所周知的方法进行了比较，以评估其准确性。实验结果表明，所提出模型的准确性与更复杂的方法（如神经网络）相比具有竞争力。

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