Succetti Federico, Rosato Antonello, Panella Massimo
Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy.
Neural Netw. 2023 Oct;167:715-729. doi: 10.1016/j.neunet.2023.08.051. Epub 2023 Sep 9.
Nowadays, solving time series prediction problems is an open and challenging task. Many solutions are based on the implementation of deep neural architectures, which are able to analyze the structure of the time series and to carry out the prediction. In this work, we present a novel deep learning scheme based on an adaptive embedding mechanism. The latter is exploited to extract a compressed representation of the input time series that is used for the subsequent forecasting. The proposed model is based on a two-layer bidirectional Long Short-Term Memory network, where the first layer performs the adaptive embedding and the second layer acts as a predictor. The performances of the proposed forecasting scheme are compared with several models in two different scenarios, considering both well-known time series and real-life application cases. The experimental results show the accuracy and the flexibility of the proposed approach, which can be used as a prediction tool for any actual application.
如今，解决时间序列预测问题是一项开放且具有挑战性的任务。许多解决方案基于深度神经架构的实现，这些架构能够分析时间序列的结构并进行预测。在这项工作中，我们提出了一种基于自适应嵌入机制的新型深度学习方案。利用该机制提取输入时间序列的压缩表示，用于后续预测。所提出的模型基于两层双向长短期记忆网络，其中第一层执行自适应嵌入，第二层作为预测器。在两种不同场景下，将所提出的预测方案的性能与几种模型进行比较，同时考虑了知名时间序列和实际应用案例。实验结果表明了所提方法的准确性和灵活性，可作为任何实际应用的预测工具。
