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 historical data when forecasting. It also uses a rectified linear unit (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 and conditionally for financial time series forecasting using the Standard & Poor’s 500 index, the volatility index, the Chicago Board Options Exchange interest rate and several exchange rates, and we extensively compare its performance with those of the well-known autoregressive model and a long short-term memory network. We show that a convolutional network is well suited to regression-type problems and is able to effectively learn dependencies in and between the series without the need for long historical time series, that it is a time-efficient and easy-to-implement alternative to recurrent-type networks, and that it tends to outperform linear and recurrent models.

中文翻译：

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用于时间序列预测的扩张卷积神经网络

我们提出了一种基于对最近的深度卷积 WaveNet 架构的改编的条件时间序列预测方法。提议的网络包含堆叠的扩张卷积，允许它在预测时访问广泛的历史数据。它还使用整流线性单元 (ReLU) 激活函数，并通过将多个卷积滤波器并行应用于单独的时间序列来执行调节，从而可以快速处理数据并利用多元时间序列之间的相关结构。我们使用标准普尔 500 指数、波动率指数无条件和有条件地测试和分析卷积网络在金融时间序列预测中的性能，芝加哥期权交易所的利率和几种汇率，我们将其性能与著名的自回归模型和长短期记忆网络的性能进行了广泛的比较。我们表明卷积网络非常适合回归类型的问题，并且能够有效地学习序列内部和序列之间的依赖关系，而无需长时间的历史时间序列，这是一种省时且易于实现的替代方案循环型网络，并且它往往优于线性和循环模型。