Time-series modeling of fishery landings in the Colombian Pacific Ocean using an ARIMA model

Selvaraj, John Josephraj; Arunachalam, Viswanathan; Coronado-Franco, Karold Viviana; Romero-Orjuela, Lizeth Viviana; Ramírez-Yara, Yessica Natalia

doi:10.1016/j.rsma.2020.101477

Cited by 23 publications

(19 citation statements)

References 13 publications

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“…The GM model uses 5–10 data to build a set of prediction models and predict a series of future data, so a set of model parameter values are obtained during the calculation process. However, if the GM model is used for long-term forecasting, the subsequent forecast values gradually lose their validity, increasing forecast errors [ 49 ]. In order to improve this shortcoming, the metabolic GM model first uses 5–10 data to build a set of prediction models, but only predicts one data value in the future.…”

Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

2022

Energy

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Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

2022

Energy

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“…So it takes a good model to be able to predict the price of gold in the future. Many statistical methods for forecasting included ARIMA (ArunKumar et al, 2021;Fan et al, 2021;M.-D. Liu et al, 2021;Selvaraj et al, 2020;Toğa et al, 2021;Yang et al, 2021), ARIMAX (Hossain et al, 2021;Li et al, 2020), multivariate time series (Koutlis et al, 2020;X. Liu & Lin, 2021;Quesada et al, 2021;Vanhoenshoven et al, 2020;R.…”

Section: Introductionmentioning

confidence: 99%

GSTARIMA Model with Missing Value for Forecasting Gold Price

Mubarak

Aslanargun

Sıklar³

2022

IJSA

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Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.

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“…For example, Stergiou [9] highly recommended a time series approach and efficiently predicted the Mullidae fishery in the Eastern Mediterranean Sea. Selvaraj et al [10] successfully developed autoregressive integrated moving average (ARIMA) models to predict fishery landings in the Colombian Pacific Ocean. Koutroumanidis et al [11], however, combined ARIMA models and fuzzy expected intervals software to forecast fishery landings in Thessaloniki, Greece.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

et al. 2021

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Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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Time-series modeling of fishery landings in the Colombian Pacific Ocean using an ARIMA model

Cited by 23 publications

References 13 publications

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach

GSTARIMA Model with Missing Value for Forecasting Gold Price

Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

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