The analysis on economic data based on time series takes an important position in the field of analysis on time-series data and is also an important task of the field of big data and artificial intelligence. Traditional time-series analysis method is of relatively weak competence in dealing with multi-series analysis. In this research, based on the problem associated with the analysis on time-series economic data, efficient handling method and model are put forward in the face of multi-series analysis task. Also, combined with the association rules, trend correlation and self-trend correlation among multiple series, a trend and correlation deep neural network model (TC-DNM) is established and then tested and verified by using three kinds of economic datasets with representativeness based on the trend analysis task handed by multi-series analysis. The results show that the model proposed in this research is effective than a number of baseline models, can be employed to achieve precision–recall balance and also possesses strong reusability. The two correlation models and joint models in this paper are of peculiarity and innovativeness.

基于时间序列的经济数据分析在时间序列数据分析领域中占有重要地位，也是大数据和人工智能领域的重要任务。传统的时间序列分析方法在处理多序列分析方面的能力相对较弱。本研究针对与时间序列经济数据分析相关的问题，提出了面对多序列分析任务的有效处理方法和模型。此外，结合多个系列之间的关联规则，趋势相关性和自我趋势相关性，建立了趋势和相关性深度神经网络模型（TC-DNM），然后使用三种具有代表性的经济数据集进行了测试和验证。多序列分析处理的趋势分析任务。结果表明，本研究提出的模型比许多基线模型有效，可以用来实现精确的召回平衡，并且具有很强的可重用性。本文的两个相关模型和联合模型具有独特性和创新性。