publication . Preprint . 2018

BRITS: Bidirectional Recurrent Imputation for Time Series

Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan;
Open Access English
  • Published: 26 May 2018
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. Th...
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free text keywords: Computer Science - Learning, Statistics - Machine Learning
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35 references, page 1 of 3

[1] C. F. Ansley and R. Kohn. On the estimation of arima models with missing values. In Time series analysis of irregularly observed data, pages 9-37. Springer, 1984.

[2] M. J. Azur, E. A. Stuart, C. Frangakis, and P. J. Leaf. Multiple imputation by chained equations: what is it and how does it work? International journal of methods in psychiatric research, 20(1):40-49, 2011.

[3] A. Basharat and M. Shah. Time series prediction by chaotic modeling of nonlinear dynamical systems. In Computer Vision, 2009 IEEE 12th International Conference on, pages 1941-1948. IEEE, 2009.

[4] B. Batres-Estrada. Deep learning for multivariate financial time series, 2015. [OpenAIRE]

[5] S. Bauer, B. Schölkopf, and J. Peters. The arrow of time in multivariate time series. In International Conference on Machine Learning, pages 2043-2051, 2016. [OpenAIRE]

[6] S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems, pages 1171-1179, 2015.

[7] M. Berglund, T. Raiko, M. Honkala, L. Kärkkäinen, A. Vetek, and J. T. Karhunen. Bidirectional recurrent neural networks as generative models. In Advances in Neural Information Processing Systems, pages 856-864, 2015. [OpenAIRE]

[8] A. P. Bradley. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7):1145-1159, 1997.

[9] P. Brakel, D. Stroobandt, and B. Schrauwen. Training energy-based models for time-series imputation. The Journal of Machine Learning Research, 14(1):2771-2797, 2013.

[10] Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu. Recurrent neural networks for multivariate time series with missing values. Scientific reports, 8(1):6085, 2018.

[11] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. [OpenAIRE]

[12] E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, and J. Sun. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference, pages 301-318, 2016.

[13] J. Friedman, T. Hastie, and R. Tibshirani. The elements of statistical learning, volume 1. Springer series in statistics Springer, Berlin, 2001.

[14] D. S. Fung. Methods for the estimation of missing values in time series. 2006.

[15] A. C. Harvey. Forecasting, structural time series models and the Kalman filter. Cambridge university press, 1990.

35 references, page 1 of 3
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. Th...
Subjects
free text keywords: Computer Science - Learning, Statistics - Machine Learning
Related Organizations
Download from
35 references, page 1 of 3

[1] C. F. Ansley and R. Kohn. On the estimation of arima models with missing values. In Time series analysis of irregularly observed data, pages 9-37. Springer, 1984.

[2] M. J. Azur, E. A. Stuart, C. Frangakis, and P. J. Leaf. Multiple imputation by chained equations: what is it and how does it work? International journal of methods in psychiatric research, 20(1):40-49, 2011.

[3] A. Basharat and M. Shah. Time series prediction by chaotic modeling of nonlinear dynamical systems. In Computer Vision, 2009 IEEE 12th International Conference on, pages 1941-1948. IEEE, 2009.

[4] B. Batres-Estrada. Deep learning for multivariate financial time series, 2015. [OpenAIRE]

[5] S. Bauer, B. Schölkopf, and J. Peters. The arrow of time in multivariate time series. In International Conference on Machine Learning, pages 2043-2051, 2016. [OpenAIRE]

[6] S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems, pages 1171-1179, 2015.

[7] M. Berglund, T. Raiko, M. Honkala, L. Kärkkäinen, A. Vetek, and J. T. Karhunen. Bidirectional recurrent neural networks as generative models. In Advances in Neural Information Processing Systems, pages 856-864, 2015. [OpenAIRE]

[8] A. P. Bradley. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7):1145-1159, 1997.

[9] P. Brakel, D. Stroobandt, and B. Schrauwen. Training energy-based models for time-series imputation. The Journal of Machine Learning Research, 14(1):2771-2797, 2013.

[10] Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu. Recurrent neural networks for multivariate time series with missing values. Scientific reports, 8(1):6085, 2018.

[11] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. [OpenAIRE]

[12] E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, and J. Sun. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference, pages 301-318, 2016.

[13] J. Friedman, T. Hastie, and R. Tibshirani. The elements of statistical learning, volume 1. Springer series in statistics Springer, Berlin, 2001.

[14] D. S. Fung. Methods for the estimation of missing values in time series. 2006.

[15] A. C. Harvey. Forecasting, structural time series models and the Kalman filter. Cambridge university press, 1990.

35 references, page 1 of 3
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