BRITS: Bidirectional Recurrent Imputation for Time Series

By Wei Cao, Jian Li, Hao Zhou, Lei Li, Yitan Li. Neural Information Processing Systems, 2018

923 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/33cb7b4d3b1e849748f4163cee821bf1306a118b

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Citations in the corpus

GP-VAE: Deep Probabilistic Time Series Imputation (2019, methodology): “BRITS [7] also uses an RNN, namely a bidirectional long short-term memory network (BiLSTM).”
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models (2022, methodology): “On the PEMS-Bay data set, SSSDS4 outperformed well-established baselines such as MICE [49], rGAIN [35], BRITS [5], and MPGRU [24] in terms of all three metrics MAE, MSE, and MRE, while it was only superseded by the recently proposed GRIN [9].”
Neural Controlled Differential Equations for Irregular Time Series (2020, methodology): “For example [6, 7, 8, 9] incorporate exponential decay between observations, [10, 11] hybridise a Gaussian process with traditional neural network models, [12] approximate the underlying continuous-time process, and [13, 14] adapt recurrent neural networks by allowing some hidden state to evolve as…”
Latent ODEs for Irregularly-Sampled Time Series (2019, methodology): “Another solution is to introduce the hidden states that decay exponentially over time [Che et al., 2018, Cao et al., 2018, Rajkomar et al., 2018].”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “introduced a bi-directional recurrent neural network to impute time series data on the UCI localization dataset [51].”
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (2020, background): “A majority of such studies [16, 10, 8, 17, 18, 19, 20] rely on LSTM or GRU to model the temporal dynamics in the time series data.”
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series (2019, background): “To overcome this issue, the main approach consists in some form of data imputation and jointly feeding the observation mask and times of observations to the recurrent network (Che et al., 2018; Choi et al., 2016a; Lipton et al., 2016; Du et al., 2016; Choi et al., 2016b; Cao et al., 2018).”
Bayesian Temporal Factorization for Multidimensional Time Series Prediction (2019, background): “Third, we would like to integrate recent advances in deep learning to better capture the complex and non-linear dynamics in modern time series data [39], [40], [41], [42], [43], [44], [45], [46].”
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series (2019, background): “(Che et al., 2018; Cao et al., 2018; Rajkomar et al., 2018; Mozer et al., 2017).”
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation (2021, methodology): “Following previous studies [7, 21], we use hourly sampled PM2.”
Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Recent years, imputation methods have been greatly for both deterministic imputation [23, 27, 154] and probabilistic imputation [65], including diffusion-based approaches.”