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All Journal Data Science: Journal of Computing and Applied Informatics Journal of Applied Data Sciences
Chi, Yeong Nain
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models Chi, Yeong Nain
Journal of Applied Data Sciences Vol 2, No 2: MAY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i2.27

Abstract

This study tried to demonstrate the role of time series models in modeling and forecasting process 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 fit model for the time series, according to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 9 neurons in the hidden layer and 6 time delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The Mixed model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities can be a better choice for modelling the time series. The comparative results revealed that the Mixed-LM model with 9 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 9 neurons in the hidden layer and 6 time delays, and the ARIMA(1,1,1)(2,0,0)12 with drift model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating local mean sea level forecast in advance. 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.
Application of Nonlinear Autoregressive Neural Network Model to Forecast Local Mean Sea Level Chi, Yeong Nain
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-8975

Abstract

The primary purpose of this study was to apply the nonlinear autoregressive neural network to model the long-term records of the monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana, as extracted from the National Oceanic and Atmospheric Administration Tides and Currents database. In this study, the empirical results revealed that the Bayesian Regularization algorithm was the best-suit training algorithm for its high regression R-value and low mean square error compared to the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for the nonlinear autoregressive neural network. Understanding past sea levels is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the existing data should be able to improve our understanding and significantly narrow our projections of ffuture sea-level changes.
Time Series Forecasting of Global Price of Soybeans using a Hybrid SARIMA and NARNN Model: Time Series Forecasting of Global Price of Soybeans Chi, Yeong Nain
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i2-5674

Abstract

Global price of soybeans has a big impact because of the trade war between the U.S. and China. Under this circumstance, price forecast is vital to facilitate efficient decisions and will play a major role in coordinating the supply and demand of soybeans globally. Hence, the primary purpose of this study was to demonstrate the role of time series models in predicting process using the time series data of monthly global price of soybeans from January 1990 to January 2021. The SARIMA and NARNN models are good at modelling linear and nonlinear problems for the time series, respectively. However, using the hybrid model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities, can be a better choice for modelling the time series. The comparative results revealed that the Hybrid-LM model with 8 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 8 neurons in the hidden layer and 3 time delays, and the SARIMA, ARIMA(0,1,3)(0,0,2)12, model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating price forecast of soybeans for the global soybean market.
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