Online Forecasting Matrix Factorization
This work addresses forecasting for high-dimensional time series with missing data in an online streaming setting, representing an incremental improvement over existing methods.
The paper tackles the problem of forecasting high-dimensional time series with missing values by developing online matrix factorization techniques to learn low-dimensional embeddings and a recursive minimum mean square error estimator for autoregressive modeling, achieving benefits demonstrated on real datasets with tens of millions of measurements.
In this paper the problem of forecasting high dimensional time series is considered. Such time series can be modeled as matrices where each column denotes a measurement. In addition, when missing values are present, low rank matrix factorization approaches are suitable for predicting future values. This paper formally defines and analyzes the forecasting problem in the online setting, i.e. where the data arrives as a stream and only a single pass is allowed. We present and analyze novel matrix factorization techniques which can learn low-dimensional embeddings effectively in an online manner. Based on these embeddings a recursive minimum mean square error estimator is derived, which learns an autoregressive model on them. Experiments with two real datasets with tens of millions of measurements show the benefits of the proposed approach.