Time-Aware Long-Short Term Memory
Regularity of the duration between consecutive elements of a sequence is a property that does not always hold. An architecture that can overcome this irregularity is necessary to increase the prediction performance.
Time Aware LSTM (T-LSTM) was designed to handle irregular elapsed times. T-LSTM is proposed to incorporate the elapsed time information into the standard LSTM architecture to be able to capture the temporal dynamics of sequential data with time irregularities. T-LSTM decomposes memory cell into short-term and long-term components, discounts the short-term memory content using a non-increasing function of the elapsed time, and then combines it with the long-term memory.
Code is compatible with tensorflow version 1.2.1 and Pyhton 2.7.13.
An example data format is given where data is stored as a list containing 3 dimensionals tensors such as [number of samples x sequence length x dimensionality].
Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou, "Patient Subtyping via Time-Aware LSTM Networks", KDD, 2017.