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Title: Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time

Paper content:
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.
The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records.
Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.
An irregularly (or unevenly) sampled time series is a sequence of samples with irregular time intervals between observations.
This class of data add a time sparsity factor when the intervals between observations are large.
Most machine learning methods do not have time comprehension, this means they only consider observation order.
This makes it harder to learn time dependencies found in time series problems.
To solve this problem recent work propose models that are able to deal with such irregularity (Lipton et al., 2016; Bahadori & Lipton, 2019; Che et al., 2018; Shukla & Marlin, 2018) , but they often rely on complex mechanisms to represent irregularity or to impute missing data.
In this paper, we introduce a novel way to represent time as a dense vector representation, which is able to improve the expressiveness of irregularly sampled data, we call it Time Embeddings (TEs).
The proposed method is based on sinusoidal functions discretized to create a continuous representation of time.
TEs can make a model capable of estimating time intervals between observations, and they do so without the addition of any trainable parameters.
We evaluate the method with a publicly available real-world dataset of irregularly sampled electronic health records called MIMIC-III (Johnson et al., 2016) .
The tests were made with two tasks: a classification task (in-hospital mortality prediction) and a regression (length of stay).
To evaluate the impact of time representation in the data expressiveness we used LSTM and SelfAttentive LSTM models.
Both are common RNN models that have been reported to achieve great performance in several time series classification problems, and specifically with the MIMIC-III dataset (Lipton et al., 2016; Shukla & Marlin, 2018; Bahadori & Lipton, 2019; Zhang et al., 2018) .
We also evaluated simpler models such as linear and logistic regression and a shallow Multi Layer Perceptron.
All models were evaluated with and without TEs to asses possible improvements.
This paper propose a novel method to represent hour time or dates as dense vectors to improve irregularly sampled time series.
It was evaluated with two different approaches and evaluated in two tasks from the MIMIC III dataset.
Our method showed some improvement with most models tested, including recurrent neural networks and classic machine learning methods.
Despite being outperformed by binary masking in some tests we believe TEs can still be an viable option.
Specially to very irregular time series and high dimensional data, were TEs can be applied by addition without increasing the input dimensionality.

assistant: text: A novel method to create dense descriptors of time (Time Embeddings) to make simple models understand temporal structures