Noisy and nonstationary real-world time series predictions (TSPs) are challenging tasks. Confronted with these challenging tasks, the predictive power of traditional shallow models is commonly not satisfactory enough. While the research on deep learning (DL) has made milestone breakthrough in recent years, and DL paradigm has gradually become indispensable for accomplishing these complex tasks. In this work, a cascading deep architecture based on weak results (DeepCascade-WR) is established, which possesses deep models’ marked capability of feature representation learning based on complex data. In DeepCascade-WR, weak prediction results are defined, innovating the forecasting mode of traditional TSP. The original data will be properly reconstituted with prior knowledge, generating attribute vectors with valid predictive information. DeepCascade-WR possesses online learning ability and effectively avoids the retraining problem, owing to the property of OS-ELM, one base model of DeepCascade-WR. Besides, ELM is exploited as another base model of DeepCascade-WR, therefore, DeepCascade-WR naturally inherits some valuable virtues from ELM, including faster training speed, better generalization ability and the avoidance of being fallen into local optima. Ultimately, in the empirical results, DeepCascade-WR demonstrates its superior predictive performance on five benchmark financial datasets, i.e., ^DJI, ^GSK, ^HSI, JOUT, and S&P 500 Index, compared with its base learners and other state-of-the-art algorithms.

中文翻译：

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DeepCascade-WR：基于弱结果的级联深度架构，用于时间序列预测

嘈杂且不稳定的现实世界时间序列预测（TSP）是具有挑战性的任务。面对这些艰巨的任务，传统浅层模型的预测能力通常不够令人满意。虽然近年来深度学习（DL）的研究取得了里程碑式的突破，但DL范式已逐渐成为完成这些复杂任务所不可缺少的。在这项工作中，建立了基于弱结果的级联深度体系结构（DeepCascade-WR），该体系结构具有基于深度模型的基于复杂数据的特征表示学习的标记能力。在DeepCascade-WR中，定义了较弱的预测结果，从而革新了传统TSP的预测模式。原始数据将使用先验知识进行适当重构，从而生成具有有效预测信息的属性向量。DeepCascade-WR具有在线学习能力，并且由于OS-ELM的特性（DeepCascade-WR的基本模型）而具有在线学习能力，并且有效地避免了再培训问题。此外，ELM被用作DeepCascade-WR的另一个基本模型，因此，DeepCascade-WR自然地继承了ELM的一些有价值的优点，包括更快的训练速度，更好的泛化能力以及避免陷入局部最优状态。最终，在实证结果中，DeepCascade-WR在五个基准金融数据集（即DJI，^ GSK，^ HSI，JOUT和S＆P 500指数）上证明了其卓越的预测性能，与基础学习者和其他状态类似。最先进的算法。ELM被用作DeepCascade-WR的另一个基本模型，因此，DeepCascade-WR自然地继承了ELM的一些有价值的优点，包括更快的训练速度，更好的泛化能力以及避免陷入局部最优状态。最终，在实证结果中，DeepCascade-WR在五个基准金融数据集（即^ DJI，^ GSK，^ HSI，JOUT和S＆P 500指数）上证明了其卓越的预测性能，与基础学习者和其他状态类似。最先进的算法。ELM被用作DeepCascade-WR的另一个基本模型，因此，DeepCascade-WR自然地继承了ELM的一些有价值的优点，包括更快的训练速度，更好的泛化能力以及避免陷入局部最优状态。最终，在实证结果中，DeepCascade-WR在五个基准金融数据集（即^ DJI，^ GSK，^ HSI，JOUT和S＆P 500指数）上证明了其卓越的预测性能，与基础学习者和其他状态类似。最先进的算法。