Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting

TL;DR

This paper introduces TSEN, a deep learning model combining RNNs and attention mechanisms to improve the accuracy of household leverage series forecasting by capturing complex temporal and spatial dependencies.

Contribution

The paper presents a novel deep learning architecture that effectively models temporal-spatial dynamics in financial time series forecasting, specifically for household leverage in China.

Findings

TSEN outperforms traditional models in forecasting accuracy.

Clustering and selecting relevant series enhance prediction performance.

The model captures complex temporal and spatial dependencies effectively.

Abstract

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.