# Conditional Time Series Forecasting with Convolutional Neural Networks

@article{Borovykh2017ConditionalTS, title={Conditional Time Series Forecasting with Convolutional Neural Networks}, author={Anastasia Borovykh and Sander M. Boht{\'e} and Cornelis W. Oosterlee}, journal={arXiv: Machine Learning}, year={2017} }

textabstractForecasting financial time series using past observations has been a significant topic of interest. [...] Key Method Effectively, we use multiple financial time series as input in the neural network, thus conditioning the forecast of a time series x(t) on both its own history as well as that of a second (or third) time series y(t). Training a model on multiple stock series allows the network to exploit the correlation structure between these series so that the network can learn the market dynamics… Expand

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