Time series data, in today's age, is ubiquitous. With the emerge of sensors, IOT devices it is spanning over all the modern aspects of life from basic household devices to self-driving cars affecting all for lives. Thus classification of time series is of unique importance in current time. With the advent of deep learning techniques , there have been influx of focus on Recurrent Neural Nets (RNN) in solving tasks related with sequence and rightly so. In this talk, I would attempt to describe the reason for success of RNN's in sequence data. Eventually we would divert towards other techniques which should be looked into when working on such problems. I will phrase examples from healthcare domain and delve into some of the other usefull techniques that can be used from Deep Learning Domain and their usefullness.

1. Looking beyond LSTMs: Alternatives in Time
Series Modelling using Neural Nets
Prepared exclusively for talk at Anthill.
Aditya Patel
aditya@stasislabs.com

2. Outline
1 Motivation
2 Hail RNN
3 Our Methodology
4 Other Methodologies
5 Takeaways

3. 1.1 Issues with Real world data
Motivation1
Missing Data Noisy Data
*Example from healthcare data
*Expectation RealityV/s

4. 1.2 Current State of Time Series Modelling
Motivation1
Similarity Measure Approaches
Dynamic Time Warping + KNN Classifier
Arima Modelling
ŷt = μ + ϕ1 yt-1 +…+ ϕp yt-p - θ1et-1 -…- θqet-q
Key Issues:
○ Time Complexity is high. Hard to implement in real time.
Neural Networks, especially Recurrent
Neural Nets
○ Considers linear functions in past data.
○ Doesn’t converge when applied on Noisy data or incomplete data.

5. • Applications:
• Music Generation
• Machine Translation
• Speech Recognition
• Application of auto encoders
2.1
Recurrent Neural Nets are all the Rage
in Sequences!!
Hail RNN2
• Recurrent Neural Nets (RNN) exploit the sequential nature of time series.
• Each output is function of a previous outputs and learns patterns via parameter sharing.
• Able to glean context by parameter sharing and unbounded nature of RNNs
2.2 Recurrent Neural Nets Highlights
Fig. 2.1 : Example of Typical RNN

6. • Susceptible to failure with noisy and missing data.
Not able to differentiate the two classes.(Fig. 2.3)
• Time Complexity: LSTM’s takes a long time to learn
the complex dependencies.
• Data requirements: LSTM’s require huge amount of
data to learn a reasonable representation.
2.3 Challenges with RNN
Hail RNN2
Fig. 2.3 : Example of Autoencoder from Stasis Vitals Dataset

7. • Highlights:
○ Takes input sequences to a kernel to create
features which calculates output taking
neighboring values into account.
○ Creates features rich in temporal history.
3.1.1 1D Convolution Neural Nets
Our Methodology - Convolutional Networks3
Equation: 1-D Convolution
* Aditya Patel et al. - http://www.scitepress.org/PublicationsDetail.aspx?ID=i4mSbjSAMY8=&t=1
*

8. • Dataset Used : MIMIC III [Opensource MIT].
• Training Examples : ~ 4000 patients with ~170000 Samples.
• Prediction Label : Mortality of the patient
• Dataset description : Physiological vitals ex. BP, Temp,
SpO2.
3.1.2 Training Summary*:
Our Methodology - Convolutional Networks3
* Aditya Patel et al. - http://www.scitepress.org/PublicationsDetail.aspx?ID=i4mSbjSAMY8=&t=1
*
Methods Layers #Hidden Units
LSTM 2 LSTM 128
1D CNN 4 convolution 64

9. Results of 1D-CNN3
• Parallel processing.
• Can glean out patterns locally.
• Works well on noisy data.
• Robust against missing data.
3.1.3 Advantages of 1D CNN
* http://www.scitepress.org/PublicationsDetail.aspx?ID=i4mSbjSAMY8=&t=1 ** On a tesla K80 GPU
1
*
Model AUC Precision Recall Time
(per epoch)
CNN 0.87 0.74 0.82 84(sec.)
RNN 0.80 0.69 0.80 494(sec.)
*
• Google - Wavenet : Generative Model for Raw Audio
• Facebook : Neural Machine Translation.
3.1.4 Other Companies employing CNN
**

10. Our Methodology - Convolutional Networks3
• The convolution in the architecture are causal.
• To accommodate longer history dilation was performed in layers.
*https://arxiv.org/pdf/1803.01271.pdf
*
3.2.1 Temporal Convolutional Networks (TCN)

11. Results - Convolutional Networks3
• Every residual block has two identical dilated causal convolutions,
𝑇(𝑛) = 𝑇(𝑛 − 1) + 2 ∗ [𝑘𝑒𝑟𝑛𝑒𝑙_𝑠𝑖𝑧𝑒(𝑛) − 1] ∗ 𝑑𝑖𝑙𝑎𝑡𝑖𝑜𝑛(𝑛)
• In our scenario, we capture 6 vital signs every 5 minutes interval. Since we are considering 2 hours
of data, it gives us 25 timestamps. 25*6 data points flattening to get a shape of (150,1).
• Best Hyperparameters : Dilation = [1,2,4] , kernel size= 3
3.2.2 Calculate Receptive Field of TCN
Table 3: Aggregated results on multi-class for predicting patient deterioration
RNN 1D CNN TCN
precision 0.77 0.83 0.81
recall 0.77 0.83 0.80
f1-score 0.74 0.83 0.80
Class of Interest Precision
TCN 0.91
1D CNN 0.83

12. 3.3 Stasis Solution
Application3

13. • Quantize the data into bins.
• Convert each bins into character.
• Use regular Natural Language Modelling techniques on it.
4.2 Bag of Words Approach
Other Noteworthy Methodologies4
• Other interesting approaches:
• Transform Time Series into Bag of Words.
• Transform Time Series to Image.
4.1 Exploration
Model Precision Recall f1- score
PAA- XGB 0.74 0.74 0.74

14. • Transform time series to polar coordinates.
• Create Gramian matrixes :
• Gramian Angular Summation Field (GASF).
• Gramian Angular Difference Fields (GADF).
• Create image from Markov Transition Fields (MTF).
• Create image by calculating Recurrence Plots (RP)
• Feed the images to tiled CNN’s.
4.3 Transform Time Series to Images
Time Series
MTFGASF
GADF RP
Model Precision Recall f1- score
GADF + CNN 0.80 0.80 0.80
Other Noteworthy Methodologies4

15. Takeaways5
5.1
• Understanding the problem is as critical as using the latest algorithms.
• With the advent of new techniques in RNN’s (example: Attention mechanisms) some of
the drawbacks mentioned in the presentation have been rectified, but we wanted to
showcase that it’s not a cure-all bullet as perceived to be.
• We need to explore existing algorithms intelligently to create robust solutions.
• 1D CNN performs well in realistic settings.
• More research is required to improve upon restrictions of Convolutional Networks for
Sequence Prediction Tasks.

16. 5.1
Q & A

17. Aditya Patel | Head of Data Science
aditya@stasislabs.com | +91 9108932213
© Copyright. 2018. Stasis Labs Inc./ Stasis Health Private Limited. All Rights Reserved.

18. Appendix
3Wikipedia
3*
Standard EWS Evaluation