Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference
This work addresses the challenge of handling redundant variables and multi-period dynamics in time series analysis, which is incremental but important for domains like finance or forecasting.
The paper tackles the problem of modeling multi-period time series by capturing dependencies across different temporal resolutions and removing redundant input variables, resulting in significant improvements in modeling and prediction performance as demonstrated on synthetic and real-world data.
In this paper, we use augmented the hierarchical latent variable model to model multi-period time series, where the dynamics of time series are governed by factors or trends in multiple periods. Previous methods based on stacked recurrent neural network (RNN) and deep belief network (DBN) models cannot model the tendencies in multiple periods, and no models for sequential data pay special attention to redundant input variables which have no or even negative impact on prediction and modeling. Applying hierarchical latent variable model with multiple transition periods, our proposed algorithm can capture dependencies in different temporal resolutions. Introducing Bayesian neural network with Horseshoe prior as input network, we can discard the redundant input variables in the optimization process, concurrently with the learning of other parts of the model. Based on experiments with both synthetic and real-world data, we show that the proposed method significantly improves the modeling and prediction performance on multi-period time series.