The autoregressive moving average (ARMA) model is a classical, and arguably one of the most studied approaches to model time series data. It has compelling theoretical properties and is widely used among practitioners. More recent deep learning approaches popularize recurrent neural networks (RNNs) and, in particular, long short-term memory (LSTM) cells that have become one of the best performing and most common building blocks in neural time series modeling. While advantageous for time series data or sequences with long-term effects, complex RNN cells are not always a must and can sometimes even be inferior to simpler recurrent approaches. In this work, we introduce the ARMA cell, a simpler, modular, and effective approach for time series modeling in neural networks. This cell can be used in any neural network architecture where recurrent structures are present and naturally handles multivariate time series using vector autoregression. We also introduce the ConvARMA cell as a natural successor for spatially-correlated time series. Our experiments show that the proposed methodology is competitive with popular alternatives in terms of performance while being more robust and compelling due to its simplicity.
翻译:自动递减移动平均(ARMA)模型是一种古典模式,可以说是模拟时间序列数据最受研究的方法之一。它具有令人信服的理论特性,在实践者中广泛使用。最近更深层次的学习方法普及了经常神经网络(RNN),特别是长期短期内存(LSTM)细胞,这些细胞已成为神经时间序列模型中最有性能和最常见的构件之一。对于具有长期影响的时间序列数据或序列来说,复杂的RNN细胞并不总是必须而且有时甚至可能比更简单的经常性方法要低。在这项工作中,我们引入ARMA细胞,这是在神经网络中进行时间序列建模的更简单、模块化和有效的方法。该细胞可以在任何神经网络结构中使用,在这些结构中,经常结构是使用矢量自动反射来自然处理多变时间序列。我们还引入了CONARMA细胞,作为空间-气候相关时间序列的自然继承物。我们的实验表明,拟议的方法在性能方面与流行的替代物更有竞争力,同时由于其简单性更强和具有说服力。