Temporal Knowledge Graph Completion (TKGC) is a challenging task of predicting missing event links at future timestamps by leveraging established temporal structural knowledge. Given the formidable generative capabilities inherent in LLMs (LLMs), this paper proposes a novel approach to conceptualize temporal link prediction as an event generation task within the context of a historical event chain. We employ efficient fine-tuning methods to make LLMs adapt to specific graph textual information and patterns discovered in temporal timelines. Furthermore, we introduce structure-based historical data augmentation and the integration of reverse knowledge to emphasize LLMs' awareness of structural information, thereby enhancing their reasoning capabilities. We conduct thorough experiments on multiple widely used datasets and find that our fine-tuned model outperforms existing embedding-based models on multiple metrics, achieving SOTA results. We also carry out sufficient ablation experiments to explore the key influencing factors when LLMs perform structured temporal knowledge inference tasks.

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