Monitoring Time Series With Missing Values: a Deep Probabilistic Approach
This addresses the challenge of robust time series monitoring for health and security systems where data may be incomplete, though it appears incremental as it builds on existing methods.
The paper tackles the problem of monitoring multivariate time series with missing values by introducing a new architecture that combines state-of-the-art forecasting methods with full probabilistic uncertainty handling, demonstrating advantages in forecasting and novelty detection on a real-world dataset.
Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible to forecast in high-dimensional time series, and identify and classify novelties early, based on subtle changes in the trends. However, mainstream approaches to multi-variate time series predictions do not handle well cases when the ongoing forecast must include uncertainty, nor they are robust to missing data. We introduce a new architecture for time series monitoring based on combination of state-of-the-art methods of forecasting in high-dimensional time series with full probabilistic handling of uncertainty. We demonstrate advantage of the architecture for time series forecasting and novelty detection, in particular with partially missing data, and empirically evaluate and compare the architecture to state-of-the-art approaches on a real-world data set.