Slope displacement forecasting with limited field data using time series model.

Slope displacement forecasting is crucial for geotechnical early warning. Conventional deterministic models cannot adapt easily to real-time monitoring, and large deep-learning models demand extensive data and compute. We leverage IBM's Tiny Time Mixer (TTM), a compact, MLP-Mixer inspired time-series model with specialized temporal and channel mixing that scales linearly with sequence length. TTM was applied to daily aggregated data from six instrumented highway embankment slopes, including soil moisture, matric suction and temperature at 1.5-5 m depths, plus rainfall. These inputs yielded 16-channel time series of roughly 950-998 points per slope, while the displacement target is available only at sparse measurement times, comprising approximately 36-38 inclinometer surveys per slope over ~ 2.75 years. A transfer-learning protocol was used; the model was sequentially fine-tuned on four slopes and evaluated zero-shot on two held-out slopes. On trained slopes, TTM forecasts proved highly accurate. For example, on the active Terry Road slope the fine-tuned model gave MAE ≈ 0.394 mm (R