GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing

TL;DR

GinAR is a novel end-to-end model that accurately forecasts multivariate time series with high missing data rates by reconstructing missing variables and dependencies using interpolation attention and adaptive graph convolution.

Contribution

The paper introduces GinAR, a new model that effectively handles variable missing in multivariate time series forecasting through innovative interpolation and graph techniques.

Findings

Outperforms 11 state-of-the-art baselines on five datasets.

Maintains accurate predictions even with 90% missing variables.

Successfully reconstructs missing data and dependencies.

Abstract

Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely forecast the future values/trends, based on the complex relationships identified from historical observations of multiple sequences. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have gradually become the theme of MTSF model as their powerful capability in mining spatial-temporal dependencies, but almost of them heavily rely on the assumption of historical data integrity. In reality, due to factors such as data collector failures and time-consuming repairment, it is extremely challenging to collect the whole historical observations without missing any variable. In this case, STGNNs can only utilize a subset of normal variables and easily suffer from the incorrect spatial-temporal dependency modeling issue, resulting in the degradation of their forecasting performance. To address the problem, in this paper, we propose a novel Graph Interpolation Attention Recursive Network (named GinAR) to precisely model the spatial-temporal dependencies over the limited collected data for forecasting. In GinAR, it consists of two key components, that is, interpolation attention and adaptive graph convolution to take place of the fully connected layer of simple recursive units, and thus are capable of recovering all missing variables and reconstructing the correct spatial-temporal dependencies for recursively modeling of multivariate time series data, respectively. Extensive experiments conducted on five real-world datasets demonstrate that GinAR outperforms 11 SOTA baselines, and even when 90% of variables are missing, it can still accurately predict the future values of all variables.