High-dimensional Time Series Prediction with Missing Values
This addresses the problem of scalable and accurate forecasting in applications like demand forecasting and climatology, offering a novel method for handling noisy, high-dimensional data with temporal dependencies.
The paper tackles high-dimensional time series prediction with missing values by proposing a temporal regularized matrix factorization (TRMF) framework that learns temporal dependencies, and it outperforms existing methods in experiments on real and synthetic data.
High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology. Often, such applications require methods that are both highly scalable, and deal with noisy data in terms of corruptions or missing values. Classical time series methods usually fall short of handling both these issues. In this paper, we propose to adapt matrix matrix completion approaches that have previously been successfully applied to large scale noisy data, but which fail to adequately model high-dimensional time series due to temporal dependencies. We present a novel temporal regularized matrix factorization (TRMF) framework which supports data-driven temporal dependency learning and enables forecasting ability to our new matrix factorization approach. TRMF is highly general, and subsumes many existing matrix factorization approaches for time series data. We make interesting connections to graph regularized matrix factorization methods in the context of learning the dependencies. Experiments on both real and synthetic data show that TRMF outperforms several existing approaches for common time series tasks.