stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series

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Revision as of 16:59, 20 November 2018 by J823zhan (talk | contribs) (Introduction)
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This page is a summary of the paper "Autoregressive Convolutional Neural Networks for Asynchronous Time Series" by Mikołaj Binkowski, Gautier Marti, Philippe Donnat. It was published at ICML in 2018.


In this paper, the authors proposed a deep convolutional network architecture called Significance-Offset Convolutional Neural Network, for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive(AR) models and gating systems used in recurrent neural networks, and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and UCI household electricity consumption dataset. This paper focused on time series with multivariate and noisy signals, especially the financial data. Financial time series are challenging to predict due to their low signal-to-noise ratio and heavy-tailed distributions. For example, same signal (e.g. price of stock) is obtained from different sources (e.g. financial news, investment bank, financial analyst etc.) in asynchronous moment of time. The investment bank with more clients can update their information more precise than the investment bank with fewer clients, then the significance of each past observations may depend on other factors that changes in time. Therefore, the traditional econometric models such as AR, VAR, VARMA[1] might not be sufficient. However, their relatively good performance could allow us to combine such linear econometric models with deep neural networks that can learn highly nonlinear relationships.

The predictability of financial dataset still remains an open problem and is discussed in various publications. ([2])

Related Work

The time series forecasting is focused on modeling the predictors of future values given their past observations, and can be expressed as a conditional probability distribution: [math]p(X_{t+d}|X_t,X_{t-1},...) = f(X_t,X_{t-1},...)[/math]