标题： 更好的预测通过融合近期和远期未来视野 显示英文标题

标题： Towards Better Forecasting by Fusing Near and Distant Future Visions

摘要： 多变量时间序列预测是机器学习中一个重要的问题，但同时也是具有挑战性的任务。大多数现有的方法仅预测单一未来时刻的时间序列值，而忽略了不同时间距离的未来时刻预测之间的相互作用。这种不足可能阻碍了模型获取足够的关于未来的相关信息，从而限制了预测的准确性。为了解决这个问题，我们提出了多级构念神经网络（MLCNN），这是一种新颖的多任务深度学习框架。受到心理学中的构念水平理论的启发，该模型旨在通过融合不同未来时间点的预测信息（即未来的愿景）来提高预测性能。我们首先使用卷积神经网络从原始数据中提取多层次的抽象表示，用于近未来和远未来的预测。然后，我们通过修改后的编码器-解码器架构建模多个预测任务之间的交互，并融合它们的未来愿景。最后，我们将传统的自回归模型与神经网络结合以解决尺度不敏感的问题。在三个真实世界的数据集上的实验表明，我们的方法相对于最先进的基线方法实现了统计上显著的改进，在RMSE指标上平均降低了4.59%，在MAE指标上平均降低了6.87%。 显示英文摘要

摘要： Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different temporal distance. Such a deficiency probably prevents the model from getting enough information about the future, thus limiting the forecasting accuracy. To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework. Inspired by the Construal Level Theory of psychology, this model aims to improve the predictive performance by fusing forecasting information (i.e., future visions) of different future time. We first use the Convolution Neural Network to extract multi-level abstract representations of the raw data for near and distant future predictions. We then model the interplay between multiple predictive tasks and fuse their future visions through a modified Encoder-Decoder architecture. Finally, we combine traditional Autoregression model with the neural network to solve the scale insensitive problem. Experiments on three real-world datasets show that our method achieves statistically significant improvements compared to the most state-of-the-art baseline methods, with average 4.59% reduction on RMSE metric and average 6.87% reduction on MAE metric.