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A quick tour of Neural Networks for Time Series

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A quick tour of Neural Networks for Time Series

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Francois Lanusse @EiffL

What are we trying  to do?

Credit: PLAsTiCC team

  • The data structure we are considering is time series:
    • Can be irregularly sampled
    • Can have large gaps
    • Can come from several modalities (e.g. different bands at different times)
       
  • From given time series, typicaly you would perform classification or regression
     
  • We want to use a Neural Network for that

A generic problem

What we will cover today:

  • Recurrent Neural Networks:
    • LSTM
    • GRU
  • ​Convolutional Neural Networks:
    • 1D CNN
    • TCN

Recurrent Neural Network Approach

Illustrations from this excellent blog: https://colah.github.io/posts/2015-08-Understanding-LSTMs/

What does an RNN look like?

\left( \begin{matrix} h_t\\ c_t \end{matrix}\right) = f_\theta \left( \begin{matrix} x_t\\ c_{t-1} \end{matrix} \right)
c_0
c_{t-1}
c_1
  •   is the RNN cell
  •   is the input at step t
  •   is the output at step t
  •   is the cell state at step t
\mathbf{A}
x_t
h_t
c_t

The problem of long-term dependencies

h_t = f_\theta( f_\theta( f_\theta(...., x_{t-3} ), x_{t-2}), x_{t-1})
  • Information has to survive through many compositions of the same function
    • Typical problems of vanishing gradients, and decaying/exploding modes
  • Practical RNNs need a mechanism to preserve long term memory

The Long Short Term Memory RNN
(Hochreiter & Schmidhuber, 1997)

The main idea: Preserve the information by default, update if necessary

1) Control of the state

  • The state of the cell can be set to 0 by the forget gate    , while the input gate  allows new information to be added to the state.

     
  • The forget gate is controlled by the previous output and new input


     
  • The input gate is controlled the same way
    The cell state update is also the result of previous output and new input
f_t
i_t

2) Cell Output

  • The output uses previous output, new input, and is gated by cell state

The Gated Recurrent Unit RNN,
(Cho et al. 2014)

Compared to the LSTM:

  • ​Merges cell state with hidden/output state
  • Merges forget and input gates into a single update gate

LSTM

Let's build an RNN-based model

RNN

x_0
c_0
h_0

RNN

x_1
c_1
h_1

RNN

x_2
c_2
h_2

RNN

x_3
c_3
h_3

Dense

\hat{y}
\mathcal{L} = \parallel y - \hat{y} \parallel_2^2

The simplest RNN regression model

import tensorflow as tf

# Create model instance
model = tf.keras.Sequential()
# Add layers to your model
model.add(layers.LSTM(128, input_shape=(10,)))
model.add(layers.Dense(32))
# Compile the model with specific optimizer and loss function
model.compile(optimizer='rmsprop', loss='mse')

RNN

x_0

RNN

x_1

RNN

x_2

RNN

x_3

Dense

\hat{y}
\mathcal{L} = \parallel y - \hat{y} \parallel_2^2

Let's go deeper! Stacked RNNs

RNN

RNN

RNN

RNN

RNN

h_0

RNN

h_1

RNN

h_2

RNN

h_3

RNN

x_0

RNN

x_1

RNN

x_2

RNN

x_3

Dense

\hat{y}
\mathcal{L} = \parallel y - \hat{y} \parallel_2^2

Causality is overrated! Bi-directional RNNs

RNN

RNN

RNN

RNN

Pooling

Some examples of RNNs in the (astro) wild

Main takeaways for RNNs

  • Naturally adapted to sequences, they do not require regularly sampled data.
     
  • Despite many improvements and practical architectures, training a recurrent neural network remains an inherently challenging task.
     
  • RNNs do not benefit from the same inductive biases as CNNs for time series
    • No built-in notion of time-scales!
    • You need to manually encode time one way or another

Convolutional Neural Networks approach

Credit: https://arxiv.org/abs/1809.04356

Convolutional Neural Network for 1D data

Several problems of this approach:

  • For sequence modeling, causality (i.e. auto-regressiveness ) of the model is important
     
  • Limited receptive field, i.e. scales accessible to the neural network

WaveNet:  Temporal (i.e. Causal) Dilated Convolutions
(van den Oord, et al. 2016)

y = f( (W \ast x)_{\downarrow 2} + b)

For a temporal convolution W is a causal filter

Examples of 1D CNNs in the (astro) wild

Main takeaways for CNNs

  • CNNs do not have the issue of long term memory
    retention

     
  • CNNs require a constant data rate,
    how do you handle irregular samples? 
    • Typically, you pad with zeros and hope
      for the best :-)
       
  • Convolutions are appropriate operations for 1D data
    • This inductive bias means you achieve high quality results with relatively low number of parameters.

Bai et al. 2018

Conclusion

  • RNNs and CNNs have both been used to analyse time series in astrophysics.
    • They can be used in many different ways with varying results depending on the application.
       
  • Properly handling time-dependency is in both cases an important factor
     
  • RNNs are so 2016... Attention is all you need

Thank you!

Bonus: Check out a complete example of star/quasar classification by LSTM here