Analyzing both temporal and spatial patterns for an accurate forecasting model for financial time series forecasting is a challenge due to the complex nature of temporal-spatial dynamics: time series from different locations often have distinct patterns; and for the same time series, patterns may vary as time goes by. Inspired by the successful applications of deep learning, we propose a new model to resolve the issues of forecasting household leverage in China. Our solution consists of multiple RNN-based layers and an attention layer: each RNN-based layer automatically learns the temporal pattern of a specific series with multivariate exogenous series, and then the attention layer learns the spatial correlative weight and obtains the global representations simultaneously. The results show that the new approach can capture the temporal-spatial dynamics of household leverage well and get more accurate and solid predictive results. More, the simulation also studies show that clustering and choosing correlative series are necessary to obtain accurate forecasting results.
翻译:由于时间空间动态的复杂性质,对金融时序预测准确预测模型的时间和空间模式进行分析是一项挑战:不同地点的时间序列往往有不同的模式;在同一时间序列中,模式可能随时间流逝而变化。在深层次学习的成功应用的启发下,我们提出了一个新的模式来解决中国家庭杠杆预测问题。我们的解决办法包括多个基于RNN的层和关注层:每个基于RNN的层自动学习一个特定序列与多种变异外源序列的时间模式,然后关注层学习空间相关权重并同时获得全球表述。结果显示,新方法可以捕捉家庭杠杆的时间空间动态,并获得更准确和可靠的预测结果。此外,模拟研究还表明,集群和选择相关序列对于获得准确的预测结果是必要的。