Monitoring Time Series With Missing Values: a Deep Probabilistic Approach

TL;DR

This paper presents a deep probabilistic architecture for monitoring multivariate time series that effectively handles missing data and quantifies uncertainty, improving forecasting and novelty detection in real-world applications.

Contribution

The paper introduces a novel deep probabilistic architecture combining advanced forecasting methods with uncertainty modeling for high-dimensional time series with missing data.

Findings

Outperforms state-of-the-art methods in forecasting accuracy.

Effectively detects novelties with partially missing data.

Demonstrates robustness and improved uncertainty quantification.

Abstract

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.