Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.

中文翻译：

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使用预期学习机从短期时间序列预测未来动态

预测时间序列在不同学科中具有重要的实际应用。在这里，我们提出了一种基于短期但高维数据的预期学习机（ALM）来实现精确的未来状态预测。从非线性动力系统理论，我们表明 ALM 可以将高维变量的近期相关/空间信息转换为任何目标变量的未来动力/时间信息，从而克服小样本问题并实现多步提前预测。由于由高维数据生成的训练样本还包含目标变量未知未来值的信息，因此称为预期学习。对真实世界数据的大量实验表明，ALM 的性能明显优于所有现有的 12 种方法。