NetDyna: Mining Networked Coevolving Time Series with Missing Values

Hairi, Hanghang Tong, Lei Ying

Abstract: This paper presents a novel algorithm for recovering missing values of co-evolving time series with partial embedded network information. The idea is to connect two sources of data (time series data and embedded network data) through a shared low dimensional latent space. The proposed algorithm, named NetDyna, is an Expectation-Maximization (EM) algorithm, and uses the Kalman filter and matrix factorization approaches to infer the missing values both in the time series and embedded network. Our experimental results on real datasets, including a Motes dataset and a Motion Capture dataset, show that (1) NetDyna outperforms other state-of-the-art algorithms, especially with partially observed network information; (2) its computational complexity scales linearly with the time duration of time series; and (3) the algorithm recovers the embedded network in addition to missing time series values.

External IDs:dblp:conf/bigdataconf/HairiTY19