BRITS: Bidirectional Recurrent Imputation for Time Series (2018-05-01T00:00:00.000000Z)

TL;DR

BRITS is a novel method based on recurrent neural networks for missing value imputation in time series data that directly learns the missing values in a bidirectional recurrent dynamical system, without any specific assumption.

Abstract

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing value imputation in time series data. Our proposed method directly learns the missing values in a bidirectional recurrent dynamical system, without any specific assumption. The imputed values are treated as variables of RNN graph and can be effectively updated during the backpropagation.BRITS has three advantages: (a) it can handle multiple correlated missing values in time series; (b) it generalizes to time series with nonlinear dynamics underlying; (c) it provides a data-driven imputation procedure and applies to general settings with missing data.We evaluate our model on three real-world datasets, including an air quality dataset, a health-care data, and a localization data for human activity. Experiments show that our model outperforms the state-of-the-art methods in both imputation and classification/regression accuracies.

Authors

Lei Li

15 papers

Hao Zhou

5 papers

Wei Cao

1 papers

TL;DR

Abstract

Authors

References36 items

DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks

Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data

Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Learning Linear Dynamical Systems from Multivariate Time Series: A Matrix Factorization Based Framework

The Arrow of Time in Multivariate Time Series

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Bidirectional Recurrent Neural Networks as Generative Models

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks

On the difficulty of training recurrent neural networks

Predicting in-hospital mortality of ICU patients: The PhysioNet/Computing in cardiology challenge 2012

Recent Techniques of Clustering of Time Series Data: A Survey

Multiple imputation by chained equations: what is it and how does it work?

An Agent-Based Approach to Care in Independent Living

Time series prediction by chaotic modeling of nonlinear dynamical systems

DynaMMo: mining and summarization of coevolving sequences with missing values

Unifying user-based and item-based collaborative filtering approaches by similarity fusion

The effects of the irregular sample and missing data in time series analysis.

The Elements of Statistical Learning

Long Short-Term Memory

The use of the area under the ROC curve in the evaluation of machine learning algorithms

Forecasting structural time series models and the kalman filter: Andrew Harvey, 1989, (Cambridge University Press), 554 pp., ISBN 0-521-32196-4

Forecasting, Structural Time Series Models and the Kalman Filter.

Multi-directional Recurrent Neural Networks : A Novel Method for Estimating Missing Data

imputeTS: Time Series Missing Value Imputation in R

Deep Learning

Deep learning for multivariate financial time series

Training energy-based models for time-series imputation

Methods for the estimation of missing values in time series

2 Non-linear time series models and dynamical systems

On the estimation of ARIMA Models with Missing Values

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