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Time series forecasting using data mining models applied to various time sequence data of a wide variety of domains has been well documented. In this work, time series of water level data recorded every hour at 'Cristobal Bay' in Panama during the years 1909-1980 are employed to construct a model(s) that can be suitable for predicting changes in sea level patterns. Four time lag assemblages of variable combinations of the time series information are fully explored to identify the optimal combinations for the dataset using a data mining tool. The results, based on the assessment using time series of Cristobal data, show that in general using cross-validation and a longer time lag period of the time series led to more accurate forecasting of the model than that of a shorter lag period of the time series. The study also suggests that data mining techniques using cross-validation and the aid of an attribute evaluator can be effectively used in modeling time series for changes in sea level at coastal areas, and changes in ecosystems that by their nature are characterized by nonlinearity and presentation of chaotic climatic changes in their physical behavior.
cfs evaluator; cross-validation; data mining; forecasting; time series; wrapper evaluator; artificial neural-networks; selection; model