关键词: 时间序列, 缺失值, 运动捕捉, 隐藏变量

Abstract: Time series data are abundant in many application areas such as motion capture, sensor networks, weather forecasting, and financial market modeling. However, missing observations are hardly rare in these real applications, thus it remains a big challenge to model time series in the presence of missing data. With occlusion in motion capture as an example, a method is proposed to handle the challenge, which makes full use of temporal continuity and spatial correlation of time series data to identify hidden variables, to mine their dynamics, and to predict and recover missing values of time series. The experimental result shows that the approach can yield the best reconstruction error and the computation time grows slowly with the input and the time duration of the motion-capture.

Key words: time series, missing value, motion capture, hidden variable