Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear behavior. Although a rich literature exists for addressing this problem, existing approaches mostly rely on first and second order properties of the time series, since they typically assume linearity of the underlying process. Often, these are inadequate for effective classification of nonlinear time series data such as Google Trends data. Given these methodological deficiencies and the abundance of nonlinear time series that persist among real-world phenomena, we introduce an approach that merges higher order spectral analysis (HOSA) with deep convolutional neural networks (CNNs) for classifying time series. The effectiveness of our approach is illustrated using simulated data and two motivating industry examples that involve Google trends data and electronic device energy consumption data.

中文翻译：

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使用基于双谱的深度卷积神经网络进行非线性时间序列分类

使用新技术进行时间序列分类最近重新兴起，并且引起了统计学家、学科领域科学家以及商业和工业决策者的兴趣。这主要是由于技术进步导致产生的大而复杂的数据量不断增加。一个鼓舞人心的例子是谷歌趋势数据，它表现出高度非线性的行为。尽管存在丰富的文献来解决这个问题，但现有方法主要依赖于时间序列的一阶和二阶属性，因为它们通常假设底层过程是线性的。通常，这些不足以有效分类非线性时间序列数据，例如 Google 趋势数据。鉴于这些方法上的缺陷以及在现实世界现象中存在的大量非线性时间序列，我们引入了一种将高阶谱分析 (HOSA) 与深度卷积神经网络 (CNN) 相结合的方法，以对时间序列进行分类。我们的方法的有效性通过模拟数据和两个涉及谷歌趋势数据和电子设备能耗数据的行业示例来说明。