Time Series Modeling and Forecasting of Monthly Mean Sea Level (1978 – 2020): SARIMA and Multilayer Perceptron Neural Network

Yeong Nain Chi (1)
(1) Department of Agriculture, Food, and Resource Sciences, University of Maryland Eastern Shore, Princess Anne, MD 21853, United States
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The primary purpose of this study was to demonstrate the role of time series model in predicting process and to pursue analysis of time series data using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box–Jenkins methodology, the ARIMA(1,1,1)(2,0,0)[12] with drift model was selected to be the best fitting model for the time series, according to the lowest AIC value in this study. Empirically, the results revealed that the MLP neural network model performed better compared to the ARIMA(1,1,1)(2,0,0)[12] with drift model at its smaller MSE value. Hence, the MLP neural network model not only can provided information which are important in decision making process related to the future sea level change impacts, but also can be employed in forecasting the future performance for local mean sea level change outcomes. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.

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Y. N. Chi, “Time Series Modeling and Forecasting of Monthly Mean Sea Level (1978 – 2020): SARIMA and Multilayer Perceptron Neural Network”, Int. J. Data. Science., vol. 3, no. 1, pp. 45-61, Jun. 2022.


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