Predicting the Unpredictable: Reproducible BiLSTM Forecasting of
  Incident Counts in the Global Terrorism Database (GTD)
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.
68f7d9ff7669bcaeecce0b3b
Oluwasegun Adegoke
claimed_verified
2025-10-22T15:44:44.122Z
6732b04ed7e101ba37feaf2f
/avatars/dc2f871a30e0fba47728f30aa683dd20.svg
Oluwasegun Adegoke
user
Davidavid4
Predicting the Unpredictable: Reproducible BiLSTM Forecasting of
  Incident Counts in the Global Terrorism Database (GTD)
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.
A Bidirectional LSTM outperforms classical and deep learning baselines in forecasting weekly terrorism incidents using the Global Terrorism Database.
Bidirectional LSTM BiLSTM LSTM-Attention RMSE MAE MAPE temporal memory training-history length spatial grain lookback size feature groups lagged counts rolling statistics geographic features casualty features