Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models

Shabri, Ani; Samsudin, Ruhaidah

doi:10.1155/2015/969450

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…[8], in his study of hybrid wavelet and adaptive neuro-fuzzy inference system for drought forecasting stated that wavelet analysis is one of the most powerful tools to study time series. In another study, [9], described wavelet analysis as a multi-decomposition analysis that provide information for time and frequency domains and provide useful decompositions of the original time series for the wavelet-transformed data to improve the power of a forecasting model. Wavelet is a tool in time series forecasting whose importance is applied by many researchers.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

Alfa*¹,

Shabri²,

Shaari³

2019

IJRTE

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This paper proposes Wavelet-Group Methods of Data Handling (W-GMDH) model to explore its ability of drought forecasting. The W-GMDH model was developed by combining Discrete Wavelet Transform (DWT) and GMDH model using the Standardized Precipitation Index (SPI) drought data for forecasting to assess the effectiveness of the new (W-GMDH) model. These methods were used on four SPI data sets (SPI3, SPI6, SPI9 and SPI12). To achieve this, a 624 month of SPI data from January 1956 to December 2008 was used and divided into two parts (80% for training and 20% for testing). The results of the W-GMDH model were then compared with the conventional GMDH model using Root Mean Square Error (RMSE), Mean Average Error (MAE) and coefficient of correlation as the performance evaluation measures. Both results of the proposed W-GMDH model and the GMDH showed very clearly that the propose method can achieve the best forecasting performance in terms of accuracy for each of the SPI data series. The key role played by the DWT is to smooth the analysis of SPI data obtained after the wavelet decomposition which is also used to decompose the SPI data into different number of component series to minimize the forecasting error. In all the results computed, the proposed model has a minimum error indicating its superiority over the GMDH model. This indicates that W-GMDH model’s performance has outweighed that of the conventional GMDH model in SPI drought forecasting. The research contributes to the discovering of viable forecasting of drought and demonstrates that the established model is good and appropriate for drought. In all the analysis W-GMDH model has the minimum error. The overall results showed that SPI12 has the minimum error among all the SPI data considered.

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Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

Alfa*¹,

Shabri²,

Shaari³

2019

IJRTE

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“…For example, the models only depict the current picture of the fishery using the time series trend and lack the power of prediction. Other models such as bootstrap, state-space [13], and Bayesian [12,13,14,15] have also been applied with little success. The time-series approach, however, has been indispensable in understanding natural resources systems and the development of better management policies [16].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Forecasting of Lake Malombe Fish Biomass and Catch Per Unit Effort (CPUE)

Makwinja¹,

Mengistou²,

Kaunda³

et al. 2020

Preprint

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Lake Malombe fish stocks have been depleted by chronic overfishing. Various management approaches (co-management, command control, and ecosystem-based management to fisheries) have been used to manage the fishery. However, the lack of an accurate predictive model has hampered their success. Therefore, we developed and tested a time series model for Lake Malombe fishery. The seasonal fish biomass and CPUE trends were first observed and both were non-stationary. The second-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), and Akaike information criterion (AIC) were estimated, which led to the identification and construction of autoregressive integrated moving average (ARIMA) models, suitable in explaining the time series and forecasting. The results showed that ARIMA (1,2,1) provided a better prediction than its counterparts. The model satisfactorily predicted that by 2032, both fish biomass and CPUE will decrease to 3204.6 tons and 59.672 respectively, signifying the potential threat to Lake Malombe fishery. The model justified the necessity of taking precautionary measures to avoid the total collapse of the fishery.

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“…These studies made a brief analysis of the correlation between variables, self-correlation, and cross-correlation using non-linear functions to find functional relationships to introduce different models [2]. On the other hand, the wok in [10] predicted the environmental variability in the anchovy fishery in the northern zone of Chile, through the development of spatio-temporal indicators of the ecosystem, statistical relationships between indicators, GIS functions (Geographical Information Systems), and ANN models, offering an integration in the prediction of anchovy abundance.With respect to other statistical techniques implemented to forecast fishing landings, there was the application of a hybrid model studied by [11], in which the potentialities of autoregressive models integrated moving averages (ARIMA) were combined with wavelet theory to enhance the precision of fishing landings' forecasts in Malaysia. Their study found that the combined model provided more accurate forecasts of fishing landing series than the individual ARIMA model.…”

mentioning

confidence: 99%

“…With respect to other statistical techniques implemented to forecast fishing landings, there was the application of a hybrid model studied by [11], in which the potentialities of autoregressive models integrated moving averages (ARIMA) were combined with wavelet theory to enhance the precision of fishing landings' forecasts in Malaysia. Their study found that the combined model provided more accurate forecasts of fishing landing series than the individual ARIMA model.…”

mentioning

confidence: 99%

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

Vivas

Allende‐Cid

Salas

et al. 2019

Entropy

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It is well known that environmental fluctuations and fishing efforts modify fishing patterns in various parts of the world. One of the most affected areas is northern Chile. The reduction of the gaps in the implementation of national fisheries’ management policies and the basic knowledge that supports the making of such decisions are crucial. That is why in this research, a transfer function method with variable coefficients is proposed to forecast monthly disembarkation of anchovies and sardines in northern Chile, taking into account the incidence of large-scale climatic variables on landings. The method uses a least squares procedure and wavelets to expand the coefficients of the transfer function. Linear estimators of the time varying coefficients are proposed, followed by a truncation of the wavelet expansion up to an appropriate scale. Finally, the estimators for the transfer function coefficients are obtained by using the inverse wavelet transformation. Research results suggest that the transfer function models with variable coefficients fit the behavior of the anchovies’ landing with great accuracy, while the use of transfer function models with constant coefficients fits sardines’ landings better. Both fisheries’ landings could be explained to a large extent from the large scale climatic variables.

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Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models

Cited by 7 publications

References 22 publications

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

Modeling and Forecasting of Lake Malombe Fish Biomass and Catch Per Unit Effort (CPUE)

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

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