Application of time series algorithms for container imbalance forecasting using event data.

The research aim is to evaluate a time series models application for container imbalance forecasting using container event data. Compare the model results and conclude the real-world application options and limitations. The research object is time series models of container imbalance forecasting and subject is performance of models for container imbalance forecasting based on event data.There are three chapters of the research. The first one is State of the Art on Empty Container Repositioning (ECR) forecasting methods and approaches. The second part is investigation of container imbalance forecasting opportunities using event data. The third part is an application of time-series methods for forecasting container imbalance, experiments with real data and attempts to develop a novel data-driven framework for event data trained time series model evaluation. The third part consists of training experiment results analysis and interpretation. The 8 different models of ARIMA, VAR, VECM algorithms were tested and evaluated by different container size and type combinations, as well of 6 different port locations. Finally, the research conclusions are followed by references and attachments.