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Temporal Knowledge Graph Completion (TKGC) is a challenging task ofpredicting missing event links at future timestamps by leveraging establishedtemporal structural knowledge. Given the formidable generative capabilitiesinherent in LLMs (LLMs), this paper proposes a novel approach to conceptualizetemporal link prediction as an event generation task within the context of ahistorical event chain. We employ efficient fine-tuning methods to make LLMsadapt to specific graph textual information and patterns discovered in temporaltimelines. Furthermore, we introduce structure-based historical dataaugmentation and the integration of reverse knowledge to emphasize LLMs'awareness of structural information, thereby enhancing their reasoningcapabilities. We conduct thorough experiments on multiple widely used datasetsand find that our fine-tuned model outperforms existing embedding-based modelson multiple metrics, achieving SOTA results. We also carry out sufficientablation experiments to explore the key influencing factors when LLMs performstructured temporal knowledge inference tasks.

Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion