Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation
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AI 根据所提供的论文材料归类：摘要直接研究 M4 时间序列集成预测及预测特征，属于预测与时间序列方向，未明确金融或因子模型。
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用于自动集成预测与解释的深度学习和线性规划
- 研究问题
- 自动生成特征和扩展候选模型池，能否改善时间序列集成预测；LSTM 特征相较传统统计特征增加了哪些预测信息？
- 方法与思路
- 作者提出 DONUT，结合 LSTM 自编码器特征、统计特征与多样模型进行加权预测，并用线性优化作事后最优组合分析。
- 材料陈述的贡献
- 摘要称在 M4 数据上优于 FFORMA，联合两类特征表现最佳；特征分析与模型池扩展分析用于解释部分准确率提升。
- 阅读关注点
- 核对训练验证划分、模型池和调参预算的可比性，并区分可部署的预测权重与使用事后信息求得的最优组合基准。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列误差数值及显著性细节，优势限于其所述实验。
原文摘要
This paper presents an ensemble forecasting method that shows strong results on the M4 Competition dataset by decreasing feature and model selection assumptions, termed DONUT (DO Not UTilize human beliefs). Our assumption reductions, primarily consisting of auto-generated features and a more diverse model pool for the ensemble, significantly outperform the statistical, feature-based ensemble method FFORMA by Montero-Manso et al. (2020). We also investigate feature extraction with a Long Short-term Memory Network (LSTM) Autoencoder and find that such features contain crucial information not captured by standard statistical feature approaches. The ensemble weighting model uses LSTM and statistical features to combine the models accurately. The analysis of feature importance and interaction shows a slight superiority for LSTM features over the statistical ones alone. Clustering analysis shows that essential LSTM features differ from most statistical features and each other. We also find that increasing the solution space of the weighting model by augmenting the ensemble with new models is something the weighting model learns to use, thus explaining part of the accuracy gains. Moreover, we present a formal ex-post-facto analysis of an optimal combination and selection for ensembles, quantifying differences through linear optimization on the M4 dataset. Our findings indicate that classical statistical time series features, such as trend and seasonality, alone do not capture all relevant information for forecasting a time series. On the contrary, our novel LSTM features contain significantly more predictive power than the statistical ones alone, but combining the two feature sets proved the best in practice.