Time-series data analysis is important because numerous real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary over time. Time-series data are generally recorded over a long period of observation with long sequences owing to their periodic characteristics and long-range dependencies over time. Thus, capturing long-range dependency is an important factor in time-series data forecasting. To solve these problems, we proposed two novel modules, Grouped Self-Attention (GSA) and Compressed Cross-Attention (CCA). With both modules, we achieved a computational space and time complexity of order $O(l)$ with a sequence length $l$ under small hyperparameter limitations, and can capture locality while considering global information. The results of experiments conducted on time-series datasets show that our proposed model efficiently exhibited reduced computational complexity and performance comparable to or better than existing methods.
翻译:时间序列数据分析之所以重要,是因为天气预报、电力消耗和股票市场等许多现实世界任务涉及预测随时间变化而变化的数据。时间序列数据一般在长时间的观察中记录,由于周期性特点和长期的长期依赖性,有很长的顺序。因此,获取长距离依赖性是时间序列数据预测的一个重要因素。为了解决这些问题,我们提出了两个新颖的模块,即集体自我注意(GSA)和压缩交叉注意(CCA ) 。两个模块,在小的超分计限制下,我们实现了序列长度为1美元(l)的计算空间和时间复杂性,可以在考虑全球信息时捕捉到地点。在时间序列数据集上进行的实验结果表明,我们拟议的模型有效地展示了与现有方法相比或更好的计算复杂性和性能。