Forecasting of dry freight index data by using machine learning algorithms

Discovery of meaningful information from the data and design of an expert system are carried out within the frame of machine learning. Supervised learning is used commonly in practical machine learning. It includes basically two stages: a) the training data are sent to as input to the classifier algorithms, b) the performance of pre-learned algorithm is tested on the testing data. And so, knowledge discovery is carried out through the data. In this study, the analysis of Lloyd data is performed by utilizing Gradient Boosted Trees and Multi-Layer Perceptron learning algorithms. Lloyd data consist of the Baltic Dry Index, Capesize Index, Panamax Index and Supramax Index values, updated daily. Accurate prediction of these data is very important in order to eliminate the risks of commercial organization. Eight datasets from Lloyd data are obtained within the frame of two scenarios: a) the last three index values in the freight index datasets; b) the last three index values in both crude oil price and freight index datasets. The results show that the models designed with Gradient Boosted Trees and Multi-Layer Perceptron algorithms are successful for Lloyd data prediction and so proved their applicability.