A data mining approach for predicting main-engine rotational speed from vessel-data measurements

In this work we face the challenge of estimating a ship's main-engine rotational speed from vessel data series, in the context of sea vessel route optimization. To this end, we study the value of different vessel data types as predictors of the engine rotational speed. As a result, we utilize speed data under a time-series view and examine how extracting locally-aware prediction models affects the learning performance. We apply two different approaches: the first utilizes clustering as a pre-processing step to the creation of many local models; the second builds upon splines to predict the target value. Given the above, we show that clustering can improve performance and demonstrate how the number of clusters affects the outcome. We also show that splines perform in a promising manner, but do not clearly outperform other methods. On the other hand, we show that spline regression combined with a Delaunay partitioning offers most competitive results.