Networked Time Series Prediction with Incomplete Data

by   Yichen Zhu, et al.

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has found a wide range of applications from intelligent transportation, environment monitoring to mobile network management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose novel Graph Temporal Attention Networks by incorporating the attention mechanism to capture both inter-time series correlations and temporal correlations. We conduct extensive experiments on three real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods except when data exhibit very low variance, in which case NETS-ImpGAN still achieves competitive performance.


page 1

page 2

page 3

page 4


SAITS: Self-Attention-based Imputation for Time Series

Missing data in time series is a pervasive problem that puts obstacles i...

Robust Dominant Periodicity Detection for Time Series with Missing Data

Periodicity detection is an important task in time series analysis, but ...

Neural Network Training with Highly Incomplete Datasets

Neural network training and validation rely on the availability of large...

Prediction with Incomplete Data under Agnostic Mask Distribution Shift

Data with missing values is ubiquitous in many applications. Recent year...

Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets

The prediction of periodical time-series remains challenging due to vari...

Temporal Graph Signal Decomposition

Temporal graph signals are multivariate time series with individual comp...

Please sign up or login with your details

Forgot password? Click here to reset