Forecasting using multivariate empirical mode decomposition - applied to iceberg drift forecast

The prediction of the movement of a floating object in the ocean, such as an iceberg, is a challenging problem. Large uncertainties in the driving forces and possibly in the geometry of the object itself prevent accurate forecasts. However, if observations of the past trajectory of the object are available the forecast can be improved considerably. This article proposes an adaptive data-driven forecast algorithm using multivariate empirical mode decomposition to handle these kinds of forecast problems. The algorithm identifies the common oscillatory modes and noise in the velocity of the floating object and its driving forces. Thereafter, it decides which mode contributes to the movement and how the future movement of each mode can be predicted best with the available information. The efficacy of the proposed forecast algorithm is shown on a real iceberg drift data set.