Forecasting in port logistics and economics using Time Series Data Mining model

Ana Ximena Halabi Echeverry, Deborah Richards, Ayse Bilgin, Jairo R. Montoya-Torres

Research output: Contribution to journalArticlepeer-review


This paper addresses the question of how to develop forecasting models resulting from business processes that can be embodied in an intelligent decision support system. Moreover the design is suitable for evolving logistics and economic situations in which ports plan or foresee to have an improved economic role. The work presented in this paper also forms one component of a conceptual intelligent decision-making support system (i-DMSS) for port integration. The key objective of this work is to offer a model-based approach to Time Series Data Mining (TSDM) based on the assumptions that the time series may be produced by an underlying model, and that its flexibility is suitable to perform multivariate time-series analysis encompassing the notion of model selection and statistical learning known as the core of forecasting systems. The interrelated activities to induce domain knowledge are specified as the data collection principles, the descriptive modelling and normative modelling. Results indicate that for the period 2001 to 2005, the commodity throughput of coffee (tons) handled in the port of Buenaventura gains importance in the prediction of the Colombian national exports of coffee, thus indicating that the port operation was able to affect the economy in this regard. The previous period was strongly affected by outliers, creating a random walk process difficult to fit but feasible to produce due to unstable conditions evidenced in the economy.
Original languageEnglish
Pages (from-to)128-139
Number of pages12
JournalJournal of network and innovative computing
Publication statusPublished - 2014


  • Time Series Data Mining
  • Forecasts
  • Port Economics
  • Port Logistics
  • Buenaventura Port


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