Explainable Temporal Knowledge Graph Forecasting Via Frequency-Tendency Modeling

The ability to forecast future events in an interpretable manner is crucial for analyzing dynamic systems. Temporal knowledge graphs (TKGs) provide a structured framework for this task, where rule-based methods are prized for their transparency. However, current approaches suffer from a recency bias, relying heavily on the latest events while overlooking information about rule activations, such as their long-term frequency and short-term tendency. To address these limitations, we introduce FETA, a novel rule-based framework for explainable temporal knowledge graphs forecasting that systematically incorporates these global temporal patterns. FETA comprises two innovative components: a Frequency Enhanced Module (FEM) that refines predictions by aggregating long-term historical signals, and a Tendency Aware Module (TAM) that captures the evolving dynamics of rule utility through the divergence between long-term and short-term behaviors. Extensive experiments on benchmark datasets, including ICEWS14, ICEWS18, ICEWS05-15, and GDELT, validate that FETA achieves new state-of-the-art results, outperforming embedding-based, rule-based, and large language model-based baselines. The strong performance in cross-dataset and low-resource settings further underscores its capability for reliable and interpretable temporal forecasting.