DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction (1904.07464v1)

Abstract: Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series. Attention-based recurrent neural networks (RNN) can effectively represent the dynamic spatio-temporal relationships between exogenous series and target series, but it only performs well in one-step time prediction and short-term time prediction. In this paper, inspired by human attention mechanism including the dual-stage two-phase (DSTP) model and the influence mechanism of target information and non-target information, we propose DSTP-based RNN (DSTP-RNN) and DSTP-RNN-2 respectively for long-term time series prediction. Specifically, we first propose the DSTP-based structure to enhance the spatial correlations between exogenous series. The first phase produces violent but decentralized response weight, while the second phase leads to stationary and concentrated response weight. Secondly, we employ multiple attentions on target series to boost the long-term dependence. Finally, we study the performance of deep spatial attention mechanism and provide experiment and interpretation. Our methods outperform nine baseline methods on four datasets in the fields of energy, finance, environment and medicine, respectively.

• The paper introduces DSTP-RNN, a novel dual-stage two-phase attention mechanism that effectively captures complex spatio-temporal dependencies across diverse datasets.
• Its methodology uses an initial phase to identify decentralized spatial correlations followed by a phase to refine these into a stable representation.
• Experimental results show that DSTP-RNN achieves lower RMSE and MAE than nine baseline models in applications ranging from finance to medicine.

An Evaluation of DSTP-RNN for Multivariate Time Series Prediction

In the domain of long-term multivariate time series prediction, the paper by Liu et al. introduces a novel approach named Dual-Stage Two-Phase Attention-Based Recurrent Neural Networks (DSTP-RNN), including its variant DSTP-RNN-Ⅱ. This research addresses the limitations of traditional methods and existing attention-based RNNs in capturing comprehensive spatio-temporal relationships essential for accurate prediction.

Problem Context and Objectives

The paper identifies the challenge of representing complex spatial correlations and spatio-temporal dependencies within multivariate time series, crucial for robust long-term forecasts. Current models, like ARIMA and standard RNNs, are generally unable to capture both spatial and long-term temporal relationships comprehensively, often delivering suboptimal performance for extended forecasting horizons.

Methodology

DSTP-RNN is grounded in the dual-stage two-phase model of human attention mechanisms, which enhances the representation of spatio-temporal dependencies. This model structures its approach around two distinct phases of attention:

1. Phase One targets capturing volatile but decentralized spatial correlations, aiming to broadly identify interactions between exogenous series and the target series.
2. Phase Two then focuses on creating a more stationary centralized representation of these interactions, refining the attention to deal with noise and irrelevant features.

Furthermore, DSTP-RNN allocates multiple attention mechanisms to boost its capacity to capture prolonged temporal dependencies. The connectivity and filtering of target and non-target information in the neural structure mimic neuronal selectivity in the human brain, supporting more precise predictions.

Experimental Validation

The paper demonstrates the efficacy of DSTP-RNN and its variant over four datasets from different fields (energy, finance, environment, and medicine) by achieving superior performance compared to nine baseline models. Numerical improvements are substantial; for instance, the RMSE and MAE of DSTP-RNN models are lower than those of ARIMA, SVR, LSTM, GRU, and even state-of-the-art models like DARNN and GeoMAN in most tested conditions.

Implications and Future Directions

The paper’s findings validate the DSTP-RNN approach as superior in reconstructing multivariate time series with complex spatial and temporal interdependencies, offering promising directions in several application domains. By outperforming existing models on diverse datasets, DSTP-RNN provides a scalable and adaptable methodology potentially transformative for industries relying on time series prediction, such as finance for stock forecasting or medicine for patient monitoring.

For future work, the authors suggest integrating DSTP-RNN with CNNs, leveraging the spatial sensitivity of CNNs for feature extraction, particularly useful in high-dimensional data settings. Additionally, resorting to a deeper investigation of spatio-temporal dynamics through simultaneous attention distribution presents another promising avenue to advance the capabilities and applications of predictive modeling further, enabling the parsing and utilization of intricate patterns within data efficiently.

Conclusion

The DSTP-RNN and its variant DSTP-RNN-Ⅱ innovate the field of multivariate time series prediction by establishing a method that more accurately captures long-term spatio-temporal dependencies. This development highlights the potential of biologically inspired computational techniques, offering both theoretical and practical advancements in understanding and predicting complex temporal systems.