  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.
