Title: IMPROVING IRREGULARLY SAMPLED TIME SERIES LEARNING WITH DENSE DESCRIPTORS OF TIME Anonymous authors Paper under double-blind review

Abstract: 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.

1. INTRODUCTION:

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 Self-Attentive 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.

2. RELATED WORK:

The problem in focus of this work is how can a machine learning method learn representations from irregularly sampled data. Irregularity is found in many different areas, as electronic health records (Yadav et al., 2018) , climate science (Schulz & Stattegger, 1997 ), ecology (Clark & Bjørnstad, 2004) , and astronomy (Scargle, 1982) . Some works deal with irregularity as a missing data problem. With time axis discretization into fixed non-overlapping intervals, those with no observations are then said to contain missing values. This approach was taken by Marlin et al. (2012), and Lipton et al. (2016) . Lipton showed how binary indicators of missingness and observation time delta can improve Recurrent Neural Network based models better than imputation, even with the sparsity of binary masks.

Despite the improvement an issue about these methods is missing the potential of how the observation time can be informative Little & Rubin (2019) . More recently Shukla & Marlin (2018) introduced a neural network model capable to learning how to interpolate missing data and avoid time discretization, by turning a irregularly sampled time series into a regular one. Bahadori & Lipton (2019) also proposed a method to improve discretization by doing a data augmentation based on temporalclustering.

Another approach is to make complex models capable of dealing with irregularities. The work of Che et al. (2018) describes a GRU (Gated Recurrent Unit) model called GRU-D. It makes use of binary missing indicators and observation time delta as an input data and incorporates them into GRU gates to control a decay rate of missing data. Bang et al. proposed a similar method using LSTM cell states to improve the decay concept.

The concept proposed in this work is similar to Lipton et al. ( 2016) and Che et al. (2018) , as they propose the use of an additional input to describe observation time deltas. But instead of using time intervals we are proposing a way to describe the exact time moment using continuous cyclic functions. This way it is possible to calculate with a linear operation the time between any two irregular observations without the need of a cumulative sum over all intermediate data, also avoiding fixed-length time discretization and interpolation noise. Another difference is that Time Embeddings are dense representations that avoid unnecessary sparsity from missingness masking.

3. METHODOLOGY:



3.1. POSITIONAL EMBEDDINGS:

The concept of Positional Embeddings (PE) was first introduced at Gehring et al. (2017) where the author used vectors to represent word positions in a sentence. It was initially proposed to improve Convolutional Neural Network ability to handle temporal data. As a CNN do not consider order the PE was introducing numerical representation of order into embedding latent space.

The Transformer (Vaswani et al., 2017) network brought back the PE to improve a neural network based only on attention modules. The model had the same issue with order modeling as it contains no recurrence, they propose a set of sinusoidal functions discretized by each relative input position. P E (pos,2i) = sin(pos/10000 2i/d model )

(1)

P E (pos,2i+1) = cos(pos/10000 2i/d model ) (2)

The equations described above have a pos variable to indicate position, i the dimension and d model is the dimension of original embedding space. This way each dimension corresponds to a sinusoidal and the model is able to learn relative positions, as argued by the authors "since for any fixed offset k, P E pos+k) can be represented as a linear function of P E pos) " (Vaswani et al., 2017) . The total dimensions of the positional embedding is defined by d model . Each wavelength form a geometric progression from 2π to 10000 • π. The biggest wavelength defines the maximum number of inputs, if a position is higher than 10000, it will start to be redundant.

3.2. TIME EMBEDDINGS:

Inspired by Transformer position representation we propose a positional embedding for irregular positions. As Vaswani et al. (2017) discretize sinusoidal functions based on positions, it is possible to discretize it based on irregular hour times or dates. Applying these time descriptors to a irregularly sample series can make the own data be time representative. 

To do it we redefine the equations based on irregular timestamps. Instead of a position indicator there is a time variable, which is continuous. The dimension of TEs (d T E ) can parameterized and a maxtime defines a maximum time that can be represented.

The relation between maximum time and TE dimension can be a limiting factor, as the maximum time increase the distance between TEs becomes smaller. To avoid this problem it is possible to increase TE dimensionality or set a reasonable maximum time.

The main pros of using TEs can be summarized as:

• Do not need any optimizable parameter, making it a model-free choice to deal with irregularity.

• Time delta can be linearly computed between two TEs, possibly improving long term dependencies recognition.

• All TEs have the same norm, avoiding big values as it is possible to happen with time delta descriptors when interval between observations are big.

4. EXPERIMENTS:

We evaluate the proposed algorithms on two benchmark tasks: in-hospital mortality and length of stay prediction. Booth tasks with the publicly available MIMIC-III dataset (Johnson et al., 2016) .

The following section we will briefly describe the data acquisition and prepossessing used, followed by the test results and discussion.

4.1. DATASET AND TRAINING DETAILS:

To assess the method performance we used the MIMIC-III benchmark dataset following the benchmark defined by Harutyunyan et al. (2017; 2019) . With the available code we extracted sequences from in-hospital stays with first 48 hours and split into training and testing set. This results in a dataset with 17, 903 training samples and 3, 236 test samples for in-hospital mortality after 48 hours task and 35, 344/6, 225 for length of stay after 24 hours.

The dataset contains 18 variables with real values and five categorical. We did our own normalization of real variables to zero mean and unit variance, categorical variables are represented with one-hot encoding. At the length of stay task we also change labels from hours to days to avoid large outputs, to report results we change it back to hours. To make the dataset even more irregular we removed randomly part of observed test data. By doing this we artificially create bigger time gaps to re-evaluate the models with an increased irregularity.

All models was trained with PyTorch (Paszke et al., 2017) on a P100, with batch size of 100 and AdamW (Loshchilov & Hutter, 2019) optimizer with amsgrad (Reddi et al., 2019) . We performed a five fold cross-validation with 10 runs on each fold. The model with best validation performance (AUC for in-hospital mortality and Mean Absolute Error for length of stay) was selected to compose the average performance for test set. We report the mean and standard error of evaluation measures in test set.

4.2. BASELINES AND TESTS:

To have a baseline we compared TEs primarily with binary masking with time interval indicators, as reported to have a good performance with RNNs in (Lipton et al., 2016) . It was compared with the proposed method with a regular LSTM (Gers et al., 1999 ) and a Self-Attentive LSTM (Lin et al., 2017) , as RNNs are reported to achieve best results with the evaluated tasks (Lipton et al., 2016; Shukla & Marlin, 2018; Bahadori & Lipton, 2019) .

TEs was tested with dimension (d T E ) of 32 and maximum time of 48 hours. As binary masking dimension and concatenated TE increase input dimension we adjusted the LSTM hidden size to keep close the number of parameters of as describe at Table 1 .

All neural models are connected to a 3 layers Multi-Layer Perceptron (MLP) with 32, 32, and 16 neurons. The last layers is a two neurons softmax for in-hospital mortality and one linear, with ReLU, for length of stay.

The Self Attention was implemented as introduced in (Lin et al., 2017) with the only difference of using uni-directional LSTMs. The attention size (d a ) was 32 and the number of attentions (r) was 8. We also used the penalization term of C = 1 -4

As the MIMIC III data are composed by multivariated series and we assume that Time Embeddings (TEs) should not be combined directly. So, we propose to use TEs in two different ways, as additional inputs, replacing missing mask, and as a latent space transformation, by adding TEs to the RNN output hidden state. To have also a baseline of non-recurrent models and assess the TE effect on them, we tested a four layer MLP and Linear/Logistic regression (linear for length of stay and logistic for in-hospital mortality task). Results for in-hospital mortality shows that self-attention seems to deteriorate the vanilla LSTM performance, but when added the TEs it got improved sufficiently to surpass it and achieve our better average result.

In the length of stay task TEs achieved better results, especially with bigger gaps at the reduced data test. TEs improved LSTM average error, but a slight worse explained variance, were binary masking had a better performance.

At Figure 3 and 4 we can see the performance of models when we randomly remove observed data from 100% to 10%. With length of stay task the LSTM with TE concatenated have a overall smaller absolute error than vanilla LSTM, being surpassed only by the binary mask. At in-hospital mortality we see a similar performance with TE SA-LSTM and LSTM with binary masking.

With non-recurrent models it is possible to observe how TEs does not rely on recurrence. It improved both linear/logistic regression and MLP.

5. CONCLUSIONS:

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. 

6. FUTURE WORK:

We see a promising future for the method proposed. We expect to extend it to improve other types of irregular time-continuous data and also evaluate how can TE improve recent models proposed for irregularly time series, like the GRU-D (Che et al., 2018) , interpolation networks (Shukla & Marlin, 2018) and Temporal-Clustering Regularization (Bahadori & Lipton, 2019) . The code for TEs results reported will be publicly available in the future.

:



Figures and Tables:

Figure 1: Sinusoidal functions discretization by time positions

Figure 2: a) Additive model b) Concatenated model

Figure 3: Evaluation of models at in-hospital mortality with observed data from 10% to 100%

Figure 4: Evaluation of models at length of stay with observed data from 10% to 100%

Recurrent models compared and parameter count

Table content: <table><tr><td>Model</td><td>Alias</td><td>h</td><td>N o of params</td></tr><tr><td>Vanilla LSTM</td><td>LSTM</td><td colspan="2">34 15.5k</td></tr><tr><td>LSTM with Binary Masking</td><td>BM + LSTM</td><td colspan="2">22 15.5k</td></tr><tr><td>LSTM with TE concatenated</td><td>catTE + LSTM</td><td colspan="2">26 15.5k</td></tr><tr><td>Self-Attentive LSTM</td><td>SA-LSTM</td><td colspan="2">32 15.5k</td></tr><tr><td colspan="2">Self-Attentive LSTM with Binary Masking BM SA-LSTM</td><td colspan="2">22 15.5k</td></tr><tr><td>Self-Attentive LSTM with TE added</td><td colspan="3">addTE + SA-LSTM 32 15.5k</td></tr></table>

Results of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Explained Variance (EV) for length of stay task and Area Under ROC-Curve (AUC-ROC) and Average Precision (AP) for in-hospital mortality task. All results are in mean(standard deviation) format

Table content: <table><tr><td>Model</td><td>24h Length-of-Stay</td><td/><td colspan="2">48h In-hospital Mortality</td></tr><tr><td>MAE</td><td>RMSE</td><td>EV</td><td>AUC-ROC</td><td>AP</td></tr><tr><td colspan="2">Lin.R/Log.R 65.829(2.001) 133.178(2.777)</td><td>0.040(0.034)</td><td colspan="2">0.783(0.003) 0.357(0.357)</td></tr><tr><td colspan="2">BM + Lin.R/Log.R 68.343(2.453) 127.579(2.044)</td><td>0.104(0.029)</td><td colspan="2">0.804(0.003) 0.369(0.369))</td></tr><tr><td colspan="2">catTE + Lin.R/Log.R 69.585(0.870) 129.484(1.220)</td><td>0.077(0.017)</td><td colspan="2">0.794(0.005) 0.359(0.359)</td></tr><tr><td colspan="3">MLP 64.983(1.695) 137.949(14.440) -0.053(0.236)</td><td colspan="2">0.807(0.002) 0.374(0.374))</td></tr><tr><td colspan="2">BM + MLP 63.181(3.120) 125.888(0.781)</td><td>0.133(0.007)</td><td colspan="2">0.804(0.004) 0.365(0.365))</td></tr><tr><td colspan="2">catTE + MLP 64.154(1.734) 129.259(2.115)</td><td>0.090(0.019)</td><td colspan="2">0.805(0.003) 0.355(0.355))</td></tr><tr><td colspan="2">LSTM 62.597(0.957) 122.284(0.266)</td><td>0.174(0.004)</td><td colspan="2">0.855(0.002) 0.481(0.481)</td></tr><tr><td colspan="2">BM + LSTM 61.666(0.702) 121.899(0.442)</td><td>0.180(0.006)</td><td colspan="2">0.854(0.004) 0.485(0.485)</td></tr><tr><td colspan="2">catTE + LSTM 62.354(0.686) 123.253(0.155)</td><td>0.164(0.002))</td><td colspan="2">0.846(0.004) 0.476(0.476)</td></tr><tr><td colspan="2">SA-LSTM 63.353(0.715) 122.976(0.215)</td><td>0.167(0.003)</td><td colspan="2">0.851(0.005) 0.448(0.448)</td></tr><tr><td colspan="2">BM + SA-LSTM 61.955(0.460) 121.667(0.089)</td><td>0.185(0.001)</td><td colspan="2">0.854(0.006) 0.454(0.454))</td></tr><tr><td colspan="2">addTE + SA-LSTM 62.591(0.321) 122.325(0.475)</td><td>0.173(0.006)</td><td colspan="2">0.856(0.003) 0.482(0.482)</td></tr><tr><td>(a) AUC-ROC</td><td/><td colspan="2">(b) Average Precision</td><td/></tr></table>

Peer review (iclr2020):
This paper examines the utility of a sinusoidal representation of timestamps for predicting patient outcomes from EHR data. It uses a representation inspired by the Transformer*s Positional Embedding, in this case applied to time and called the Time Embedding. The same idea was recently used in a pre-print called *TAPER: Time-Aware Patient EHR Representation” on the same task (and even the same dataset). It’s possible that this paper is a followup by those authors or maybe it’s convergent thinking in the field of ML4Health. In any case, the idea is relatively simple and intuitively could be useful for certain kinds of temporal events. This paper does a cursory evaluation of its utility on the MIMIC-III EHR dataset and shows that the addition of time embeddings is typically slightly worse than using binary masking of missing time steps. It’s totally fine to arrive at a negative result! However, we should learn more from this evaluation than simply “we tried an idea and it lost to an alternative”. To make this paper successful the authors should evaluate on multiple datasets, evaluate different granularities of time (hours vs. days vs. months), and imitate Lipton 2016 in examining baselines such as forward filling imputation of missing data. As it stands the paper doesn’t add enough to our understanding of when a sinusoidal embedding of time would or wouldn’t help. Unclear phrasing and/or grammatical nits: * *This makes it harder to learn time dependencies found in time series problems.” — do you mean e.g. time of day of an event or the degree of delay between two events, or some other kind of “time dependencies”? * *The tests were made with two tasks” — maybe better to say that you evaluate on two tasks? * *The problem in focus of this work is how can a machine learning method learn representations from irregularly sampled data.” Repeats introduction, or “The problem in focus of this work” isn’t quite right grammatically. * *even with the sparsity of binary masks.” — is this talking about the low frequency of observed events? * *Despite the improvement an issue about these methods is missing the potential of how the observation time can be informative” — the improvement of what over what? Should this sentence be part of the previous paragraph? * *also proposed a method to improve discretization” — discretization of what? * *Another approach is to make complex models capable of dealing with irregularities” — does this mean models that are somehow more complicated or those that somehow represent sparse time with complex numbers? * *To make the dataset even more irregular we removed randomly part of observed test data” — missing “the”? * *In the length of stay task TEs achieved better results, especially with bigger gaps at the reduced data test” — this should come after some description of figures 3 & 4, since thus far the reduced data results weren’t described. Typos: * “asses” -> “assess” * *were binary masking had a better performance.” -> “where binary masking…* * *Specially to very irregular time series and high dimensional data” -> “Especially* * *were TEs can be applied by addition without increasing the input dimensionality” -> “where*