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Autoregressive Convolutional Neural Networks for Asynchronous Time Series

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In this talk, we present a CNN architecture for predicting autoregressive asynchronous time series. We illustrate its application on predicting traders’ quotes of credit default swaps (proprietary dataset from Hellebore Capital), and on artificial time series. The paper is available there: http://proceedings.mlr.press/v80/binkowski18a/binkowski18a.pdf

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Autoregressive Convolutional Neural Networks for Asynchronous Time Series

  1. 1. Autoregressive Convolutional Neural Networks for Asynchronous Time Series Hong Kong Machine Learning Meetup - Season 1 Episode 1 Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat Imperial College London, Ecole Polytechnique, Hellebore Capital 18 July 2018 HELLEBORECAPITAL Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 1 / 10
  2. 2. Introduction Problem: Many real-world time series are asynchronous, i.e. the durations between consecutive observations are irregular/random or the separate dimensions are not observed simultaneously. Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 2 / 10
  3. 3. Introduction Problem: Many real-world time series are asynchronous, i.e. the durations between consecutive observations are irregular/random or the separate dimensions are not observed simultaneously. At the same time: time series models usually require both regularity of observations and simultaneous sampling of all dimensions, continuous-time models often require simultaneous sampling. Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 2 / 10
  4. 4. Introduction Problem: Many real-world time series are asynchronous, i.e. the durations between consecutive observations are irregular/random or the separate dimensions are not observed simultaneously. At the same time: time series models usually require both regularity of observations and simultaneous sampling of all dimensions, continuous-time models often require simultaneous sampling. Numerous interpolation methods have been developed for preprocessing of asynchronous series. However,... Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 2 / 10
  5. 5. Drawbacks of synchronous sampling ... every interpolation method leads to either increase in the number of data points or loss of data. 0 20 40 60 80 100 original series Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 3 / 10
  6. 6. Drawbacks of synchronous sampling ... every interpolation method leads to either increase in the number of data points or loss of data. 0 20 40 60 80 100 original series frequency = 10s; information loss But the situation can be much worse... Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 3 / 10
  7. 7. Drawbacks of synchronous sampling ... every interpolation method leads to either increase in the number of data points or loss of data. 0 20 40 60 80 100 original series frequency = 10s; information loss frequency = 1s; 12x more points But the situation can be much worse... Mikolaj Bi´nkowski, Gautier Marti, Philippe Donnat (Imperial College)CNNs for Asynchronous Time Series 18 July 2018 3 / 10
  8. 8. Drawbacks of synchronous sampling

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