  Time series synthesis is an important research topic in the field of deep
learning, which can be used for data augmentation. Time series data types can
be broadly classified into regular or irregular. However, there are no existing
generative models that show good performance for both types without any model
changes. Therefore, we present a general purpose model capable of synthesizing
regular and irregular time series data. To our knowledge, we are the first
designing a general purpose time series synthesis model, which is one of the
most challenging settings for time series synthesis. To this end, we design a
generative adversarial network-based method, where many related techniques are
carefully integrated into a single framework, ranging from neural
ordinary/controlled differential equations to continuous time-flow processes.
Our method outperforms all existing methods.
