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Temporal complex event forecasting aims to predict the future events giventhe observed events from history. Most formulations of temporal complex eventare unstructured or without extensive temporal information, resulting ininferior representations and limited forecasting capabilities. To bridge thesegaps, we innovatively introduce the formulation of Structured, Complex, andTime-complete temporal event (SCTc-TE). Following this comprehensiveformulation, we develop a fully automated pipeline and construct a large-scaledataset named MidEast-TE from about 0.6 million news articles. This datasetfocuses on the cooperation and conflict events among countries mainly in theMidEast region from 2015 to 2022. Not limited to the dataset construction, moreimportantly, we advance the forecasting methods by discriminating the crucialroles of various contextual information, i.e., local and global contexts.Thereby, we propose a novel method LoGo that is able to take advantage of bothLocal and Global contexts for SCTc-TE forecasting. We evaluate our proposedapproach on both our proposed MidEast-TE dataset and the original GDELT-TEdataset. Experimental results demonstrate the effectiveness of our forecastingmodel LoGo. The code and datasets are released viahttps://github.com/yecchen/GDELT-ComplexEvent.

SCTc-TE: A Comprehensive Formulation and Benchmark for Temporal Event Forecasting