There exist several data-driven approaches that enable us model time series data including traditional regression-based modeling approaches (i.e., ARIMA). Recently, deep learning techniques have been introduced and explored in the context of time series analysis and prediction. A major research question to ask is the performance of these many variations of deep learning techniques in predicting time series data. This paper compares two prominent deep learning modeling techniques. The Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) and the convolutional Neural Network (CNN)-based Temporal Convolutional Networks (TCN) are compared and their performance and training time are reported. According to our experimental results, both modeling techniques perform comparably having TCN-based models outperform LSTM slightly. Moreover, the CNN-based TCN model builds a stable model faster than the RNN-based LSTM models.

翻译：目前有几种数据驱动方法,使我们能够模拟时间序列数据,包括传统的回归模型方法(如ARIMA)。最近,在时间序列分析和预测方面引进和探索了深层次学习技术。要问的一个主要研究问题是,在预测时间序列数据方面深层次学习技术的这些变化的绩效。本文件比较了两种突出的深层次学习模型技术:以经常性神经网络为基础的长期短期内存(RNN)和以动态神经网络为基础的时空变迁网络(CNN)进行了比较,并报告了其性能和培训时间。根据我们的实验结果,两种建模技术的TCN模型比LSTM模型略微优小。此外,以CNNN为基础的TCN模型比以RN为基础的LSTM模型更快地构建了一个稳定的模型。