Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series
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融合序列神经网络与统计方法改进时间序列预测的两条路径
- 研究问题
- 如何把统计模型提供的结构信息与数据驱动学习结合，以改进不同稳定程度时间序列的预测，而不只是分别使用两类方法？
- 方法与思路
- 作者提出分解与特征提取两条路径：按线性稳定、非线性稳定和不稳定成分分别建模，或把统计特征作为机器学习的额外输入。
- 材料陈述的贡献
- 摘要称两类融合方法在所考察时间序列上优于分别使用模型或学习的方法，报告改进可超过百分之六十，但未说明具体指标。
- 阅读关注点
- 应核对稳定性和分解的定义、统计特征提取是否使用未来信息，以及预测窗口、基准方法和百分比改进的计算口径。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明数据、误差指标与验证方案，所述改进幅度不能直接推广到其他序列。
原文摘要
Statistic modeling and data-driven learning are the two vital fields that attract many attentions. Statistic models intend to capture and interpret the relationships among variables, while data-based learning attempt to extract information directly from the data without pre-processing through complex models. Given the extensive studies in both fields, a subtle issue is how to properly integrate data based methods with existing knowledge or models. In this paper, based on the time series data, we propose two different directions to integrate the two, a decomposition-based method and a method exploiting the statistic extraction of data features. The first one decomposes the data into linear stable, nonlinear stable and unstable parts, where suitable statistical models are used for the linear stable and nonlinear stable parts while the appropriate machine learning tools are used for the unstable parts. The second one applies statistic models to extract statistics features of data and feed them as additional inputs into the machine learning platform for training. The most critical and challenging thing is how to determine and extract the valuable information from mathematical or statistical models to boost the performance of machine learning algorithms. We evaluate the proposal using time series data with varying degrees of stability. Performance results show that both methods can outperform existing schemes that use models and learning separately, and the improvements can be over 60%. Both our proposed methods are promising in bridging the gap between model-based and data-driven schemes and integrating the two to provide an overall higher learning performance.