Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand needs to be forecast might be as large as 50,000. In addition, the data is generally noisy and full of missing values. Thus, modern applications require methods that are highly scalable, and can deal with noisy data in terms of corruptions or missing values. However, classical time series methods usually fall short of handling these issues.

Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand needs to be forecast might be as large as 50,000. In addition, the data is generally noisy and full of missing values. Thus, modern applications require methods that are highly scalable, and can deal with noisy data in terms of corruptions or missing values. However, classical time series methods usually fall short of handling these issues.

Two popularly used time series prediction models, autoregressive (AR) and dynamic linear models (DLM), are both time consuming to learn, especially for high dimensional time series prediction problem with missing values. However, matrix factorization is relatively efficient for large-scale matrix. The authors model the high dimensional time series as matrix and induce the constraints as regularization terms, then formulate the problem as a regularized matrix factorization problem and solve it by adopting the off-the-shelf solvers. The temporal regularized matrix factorization(TRMF) framework proposed by the paper sounds interesting. Inherited from the properties of MF, TRMF is able to deal with missing values and can be scalable to high-dimensional time series datasets.

Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand needs to be forecast might be as large as 50,000. In addition, the data is generally noisy and full of missing values. Thus, modern applications require methods that are highly scalable, and can deal with noisy data in terms of corruptions or missing values. However, classical time series methods usually fall short of handling these issues.