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基于Transformer的时间序列插补技术研究
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Abstract:
本文旨在解决多元时间序列数据中的缺失值插补问题，提升时间序列数据插补的效果。时间序列数据是反映随时间变化的随机变量的结果，在物联网应用中得到广泛应用。然而，数据缺失问题是时间序列处理中的一个重要挑战，因为大多数下游算法需要完整的数据进行训练。本文通过总结以往时间序列建模过程中采用的插补方法，改进了一种基于Transformer模型的插补模型，并在多个数据集中验证了本文中插补模型的效果。通过本文的研究，可提高时间序列预测的准确性和实用性，对于物联网应用和其他领域中的时间序列分析具有一定的实用价值。
This article aims to address the issue of missing value imputation in multivariate time series data to enhance the effectiveness of imputation. Time series data, widely utilized in Internet of Things (IoT) applications, reflects the outcomes of random variables changing over time. However, data missingness poses a significant challenge in time series processing, as most downstream algorithms require complete data for training. By summarizing past imputation methods used in time series modeling and improving a Transformer-based imputation model, this paper validates the effectiveness of the proposed imputation model across multiple datasets. The research presented in this paper can improve the accuracy and practicality of time series prediction, providing practical value for time series analysis in IoT applications and other domains.
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