Conditional Time Series Forecasting with Convolutional Neural Networks

Forecasting financial time series using past observations has been a topic of significant interest for obvious reasons. It is well known that while temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the nonlinear trends and noise present in the series. In developing models for forecasting financial data it is desirable that these will be both able to learn nonlinear dependencies in the data as well as have a high noise resistance. Feedforward neural networks have been a popular way of learning the dependencies in the data, by e.g. using multiple inputs from the past to make a prediction for the future time step, see [3]. One downside of classical feedforward neural networks is that a large sample size of data is required to obtain a stable forecasting result. The main focus of this paper is on multivariate time series forecasting, specifically financial time series. In particular, we forecast time series conditional on other, related series.