Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multi-variate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these challenges, we propose novel STMPANets, which are capable of perceiving time-varying spatiotemporal missing patterns to refine the forecasting sequences. Specifically, we decompose the series into seasonal trend components, allowing STMPANets to highlight inherent sequence properties and adapt to missing patterns. We then propose a Multi-granularity Conditional Partial TCN (MGCPT) to regulate the imputation rate of missing values over time, modeling temporal correlation. Additionally, we design an Adaptive Dynamic GCN (ADGCN) to capture spatial dependencies by perceiving dynamic missing patterns. Extensive experiments demonstrate that STMPANets outperform state-of-the-art models.

I. Introduction

The ongoing rapid deployment of IoT and edge device systems has led to the generation of a large amount of time series data [1]. Due to incomplete monitoring of sensors, these time series often contain a large number of missing values, making them difficult to directly utilize [2]. Effectively modeling time series with missing values for prediction is a challenging problem [3]. In this context, Multivariate Time Series Forecasting (MTSF) offers a more comprehensive approach by simultaneously considering the interdependencies among multiple time series. However, when missing values are prevalent, traditional MTSF models struggle to capture temporal and spatial dependencies from past to future effectively [4]. To this end, modeling MTSFMV involves two steps: (i) performing imputation around missing values to alleviate data sparsity, and (ii) extracting temporal and spatial dependencies using known and imputed values. Ignoring either of these two steps will lead to degraded performance [5].