預測不可預測之事：基於雙向長短期記憶網絡（BiLSTM）的全球恐怖主義數據庫（GTD）事件計數可重現性預測

Predicting the Unpredictable: Reproducible BiLSTM Forecasting of Incident Counts in the Global Terrorism Database (GTD)

October 16, 2025

作者: Oluwasegun Adegoke

cs.AI

摘要

我們利用全球恐怖主義數據庫（GTD，1970-2016）研究每週恐怖事件數量的短期預測。我們構建了一個可重現的流程，採用固定的時間劃分，並將雙向長短期記憶網絡（BiLSTM）與強力的經典基準（季節性樸素模型、線性/ARIMA模型）以及一個深度LSTM-注意力基線進行比較。在保留的測試集上，BiLSTM達到了6.38的均方根誤差（RMSE），優於LSTM-注意力模型（9.19；提升30.6%）和線性滯後回歸基線（RMSE提升35.4%），同時在平均絕對誤差（MAE）和平均絕對百分比誤差（MAPE）上也取得了並行的改進。通過對時間記憶、訓練歷史長度、空間粒度、回顧窗口大小及特徵組的消融實驗表明，基於長期歷史數據訓練的模型泛化能力最佳；適中的回顧窗口（20-30週）提供了強有力的上下文信息；而雙向編碼對於捕捉窗口內的積累與後續模式至關重要。特徵組分析指出，短期結構（滯後計數與滾動統計）貢獻最大，地理與傷亡特徵則帶來額外的提升。我們公開了代碼、配置及簡潔的結果表格，並提供了一份數據/倫理聲明，記錄了GTD的許可及僅限研究使用的規定。總體而言，本研究為GTD事件預測提供了一個透明且超越基線的參考框架。

English

We study short-horizon forecasting of weekly terrorism incident counts using the Global Terrorism Database (GTD, 1970--2016). We build a reproducible pipeline with fixed time-based splits and evaluate a Bidirectional LSTM (BiLSTM) against strong classical anchors (seasonal-naive, linear/ARIMA) and a deep LSTM-Attention baseline. On the held-out test set, the BiLSTM attains RMSE 6.38, outperforming LSTM-Attention (9.19; +30.6\%) and a linear lag-regression baseline (+35.4\% RMSE gain), with parallel improvements in MAE and MAPE. Ablations varying temporal memory, training-history length, spatial grain, lookback size, and feature groups show that models trained on long historical data generalize best; a moderate lookback (20--30 weeks) provides strong context; and bidirectional encoding is critical for capturing both build-up and aftermath patterns within the window. Feature-group analysis indicates that short-horizon structure (lagged counts and rolling statistics) contributes most, with geographic and casualty features adding incremental lift. We release code, configs, and compact result tables, and provide a data/ethics statement documenting GTD licensing and research-only use. Overall, the study offers a transparent, baseline-beating reference for GTD incident forecasting.