Unified recurrent neural network for many feature types

There are time series that are amenable to recurrent neural network (RNN) solutions when treated as sequences, but some series, e.g. In order to address such situations, we introduce a unified RNN that handles five different feature types, each in a different manner. Our RNN framework separates sequential features into two groups dependent on their frequency, which we call sparse and dense features, and which affect cell updates differently. Further, we also incorporate time features at the sequential level that relate to the time between specified events in the sequence and are used to modify the cell's memory state. We also include two types of static (whole sequence level) features, one related to time and one not, which are combined with the encoder output. The experiments show that the modeling framework proposed does increase performance compared to standard cells. The study of time series has a long history and the literature for it covers many different methods (Hamilton (1994)). The study of asynchronous time series is an important subset of this. Asynchronous time series are series for which features are sampled at irregular time intervals, and at any given time step new values of any subset of features may be present. When a feature does not change values often it can be treated as being present only at times of change.