Conditional time series forecasting with convolutional neural networks
Anastasia Borovykh Dipartimento di Matematica, Università di Bologna, Bologna, Italy. e-mail: Sander Bohte Centrum Wiskunde、Informatica, Amsterdam, The Netherlands. Cornelis W. Oosterlee Centrum Wiskunde、Informatica, Amsterdam, The Netherlands. e-mail: University of Technology, Delft, The Netherlands
Abstract
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 outper
中文速览
用扩张卷积（dilated convolution）来预测金融时间序列，这是一个长期被循环神经网络垄断的领域。研究者将最初为音频生成设计的 WaveNet 架构加以改造，用 ReLU 替换原有的门控激活函数，并引入参数化跳跃连接，使网络能同时接收目标序列和多条相关序列作为条件输入，从而利用多元金融序列之间的相关结构。在 S&P500 指数、波动率指数 VIX、CBOE 利率及多个汇率的实验中，这一卷积网络在预测精度上普遍优于经典自回归模型和 LSTM，且训练速度更快、实现更简单。这项工作的重要性在于：它首次系统证明了卷积网络可以有效应对长度有限、噪声极高的金融时间序列预测，为从业者提供了一种比循环网络更轻量却同样强大的多元时序预测工具。
原文 arXiv:1703.04691;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1703.04691v5