In many cases, the correlation between time series has a certain lag effect. To study the lag correlation between two time series variables, we select London Metal Exchange (LME) nickel futures and spot prices from 3 January 2008 to 29 December 2017 as sample data to carry out stationarity tests, cointegration tests and Granger causality tests to determine the stationarity and correlation of two time series. Then, we use the method of combining the distributed lag model and sliding window method to construct a network. We select the best sliding window length through a sensitivity test. The time series is reconstructed into a complex network by taking the types of patterns as the nodes and the conduction relationship between the patterns as the edges. The number of transitions between patterns is defined as the weight of the edge. The results show that the spot price changes are caused by the change in nickel futures price and that the optimal sliding window length is 64. Additionally, 12 types of patterns account for a large proportion of the patterns in the network. Six patterns are the main intermediaries of pattern transmission and appear centrally with the change in the market environment. Therefore, the relationship model between these futures and spot prices has remained stable for a long time. Combining the positive and negative news of the market, we identify the timing of the change in the relationship model and can use this approach to improve the accuracy of early warning methods. This study provides a method to construct a complex network using a distributed lag model, which can help analyze two real time series variables with lag correlation.

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