  We present a method for conditional time series forecasting based on an
adaptation of the recent deep convolutional WaveNet architecture. The proposed
network contains stacks of dilated convolutions that allow it to access a broad
range of history when forecasting, a ReLU activation function and conditioning
is performed by applying multiple convolutional filters in parallel to separate
time series which allows for the fast processing of data and the exploitation
of the correlation structure between the multivariate time series. We test and
analyze the performance of the convolutional network both unconditionally as
well as conditionally for financial time series forecasting using the S&P500,
the volatility index, the CBOE interest rate and several exchange rates and
extensively compare it to the performance of the well-known autoregressive
model and a long-short term memory network. We show that a convolutional
network is well-suited for regression-type problems and is able to effectively
learn dependencies in and between the series without the need for long
historical time series, is a time-efficient and easy to implement alternative
to recurrent-type networks and tends to outperform linear and recurrent models.
