[论文解读] Time-series Imputation and Prediction with Bi-Directional Generative Adversarial Networks

Mehak Gupta, Rahmatollah Beheshti|arXiv (Cornell University)|Sep 18, 2020

Machine Learning in Healthcare参考文献 28被引用 9

一句话总结

本文提出Bi-GAN，一种双向生成对抗网络，可联合对不规则、不完整且长度可变的多变量时间序列数据进行时间序列插补与预测。通过利用双向循环生成器填补缺失值，并借助判别器提升生成结果的真实性，该模型实现了无需预设时间窗口的灵活任意时间点预测，在儿科健康记录和公开数据集上的体质指数（BMI）预测任务中表现优于当前最先进方法。

ABSTRACT

Multivariate time-series data are used in many classification and regression predictive tasks, and recurrent models have been widely used for such tasks. Most common recurrent models assume that time-series data elements are of equal length and the ordered observations are recorded at regular intervals. However, real-world time-series data have neither a similar length nor a same number of observations. They also have missing entries, which hinders the performance of predictive tasks. In this paper, we approach these issues by presenting a model for the combined task of imputing and predicting values for the irregularly observed and varying length time-series data with missing entries. Our proposed model (Bi-GAN) uses a bidirectional recurrent network in a generative adversarial setting. The generator is a bidirectional recurrent network that receives actual incomplete data and imputes the missing values. The discriminator attempts to discriminate between the actual and the imputed values in the output of the generator. Our model learns how to impute missing elements in-between (imputation) or outside of the input time steps (prediction), hence working as an effective any-time prediction tool for time-series data. Our method has three advantages to the state-of-the-art methods in the field: (a) single model can be used for both imputation and prediction tasks; (b) it can perform prediction task for time-series of varying length with missing data; (c) it does not require to know the observation and prediction time window during training which provides a flexible length of prediction window for both long-term and short-term predictions. We evaluate our model on two public datasets and on another large real-world electronic health records dataset to impute and predict body mass index (BMI) values in children and show its superior performance in both settings.

研究动机与目标

• 解决预测建模中不规则采样多变量时间序列数据缺失的问题。
• 开发一种统一的深度学习框架，实现插补与预测，且无需固定输入或预测窗口长度。
• 通过训练时无需指定预测时域，实现灵活的长期或短期预测。
• 提升真实世界时间序列数据（尤其是儿科BMI预测等医疗应用）上的性能表现。
• 证明单一模型可有效处理复杂、不完整时间序列场景下的插补与预测任务。

提出的方法

• 生成器为双向循环神经网络（Bi-RNN），接收不完整的时间序列输入，并生成缺失值的合理插补结果。
• 判别器经过训练，能够区分真实时间序列数据与生成器生成的（插补后）序列。
• 通过极小化极大对抗目标，端到端联合优化生成器与判别器。
• 双向结构使生成器能够同时利用过去与未来的上下文信息，提升插补与预测的准确性。
• 训练过程中无需知晓预测窗口长度，从而支持动态灵活的预测。
• 该框架支持序列内插补与非顺序预测，适用于任意时间点预测任务。

实验结果

研究问题

• RQ1 单一深度学习模型能否有效处理不规则、不完整且长度可变的时间序列数据的插补与预测？
• RQ2 与单向模型相比，双向结构在插补与预测准确性方面有何提升？
• RQ3 该模型在无需预设时间窗口的情况下，对长期与短期预测的泛化能力如何？
• RQ4 在真实世界数据集上，Bi-GAN在插补与预测性能方面与当前最先进方法相比表现如何？
• RQ5 该模型能否在复杂真实时间序列（如电子健康记录）上保持高性能？

主要发现

• 在两个公开数据集和一个大规模真实世界电子健康记录数据集上，Bi-GAN在插补与预测任务上均优于当前最先进方法。
• 模型展现出强大的泛化能力，无需在训练时指定窗口长度，即可实现对短期与长期预测的高精度。
• 双向生成器通过捕捉过去与未来时间点的上下文依赖关系，显著提升了插补质量。
• 对抗训练框架增强了生成序列的真实性，从而带来更可靠的预测结果。
• 在儿科BMI预测任务中，Bi-GAN在平均绝对误差与均方根误差指标上均优于现有方法。
• 该模型无需调整网络结构或超参数即可处理带缺失值的可变长度序列，凸显其鲁棒性与灵活性。