Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time

Understand

Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals.

• Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance.
• This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings.
• As a data input method it and can be applied to most machine learning models.