Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Networks (RNNs), RNNs have been generalised to have continuous-time hidden dynamics defined by a Neural Ordinary Differential Equation (Neural ODE), leading to the ODE-RNN model. Another approach that provides a better modelling is that of the Latent ODE model, which constructs a continuous-time model where a latent state is defined at all times. The Latent ODE model uses a standard RNN as the encoder and a Neural ODE as the decoder. However, since the RNN encoder leads to difficulties with missing data and ill-defined latent variables, a Latent ODE-RNN model has recently been proposed that uses a ODE-RNN model as the encoder instead.

中文翻译：

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使用潜在的 ODE-LSTM 方法增强连续时间序列建模

由于连续时间序列 (CTS) 具有不规则采样率和高频采样等动态特性，因此在许多应用中都有应用。由于不规则采样率的 CTS 很难用标准循环神经网络 (RNN) 进行建模，因此 RNN 已被推广为具有由神经常微分方程 (神经 ODE) 定义的连续时间隐藏动态，从而产生了 ODE-RNN 模型。另一种提供更好建模的方法是潜在 ODE 模型，它构建了一个连续时间模型，其中始终定义潜在状态。潜在 ODE 模型使用标准 RNN 作为编码器，使用神经 ODE 作为解码器。然而，由于 RNN 编码器会导致数据丢失和潜在变量定义不明确的困难，因此最近提出了一种使用 ODE-RNN 模型作为编码器的潜在 ODE-RNN 模型。