Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches

Czerwinski, Ivone Alejandra; Gutiérrez‐Estrada, Juan Carlos; Hernando-Casal, José Antonio

doi:10.1016/j.fishres.2007.05.006

Cited by 39 publications

(32 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It was further observed that the coefficients of the parameters of ARIMA (1,1,0) model were significant. According to Czerwinski et al (2007), the model which indicate lowest normalized BIC and is significant (p<0.05) is a better model in terms of forecasting performance than with large normalized BIC. Estimates of the selected ARIMA (1,1,0) model are presented in Table 3.…”

Section: Model Selectionmentioning

confidence: 96%

“…It was further interesting to note that Maximum Absolute Percentage Error (MAXAPE) and Maximum Absolute Error (MAXAE) expressed as percentage was very small in ARIMA (1,1,0) model indicating overall good model fit. According to Czerwinski et al (2007), the best model should have adequate accuracy measures (RMSE, MAE) and lowest Normalised BIC for it to have accurate forecasts. Therefore, ARIMA (1,1,0) model was selected because it had lowest RMSE, MAE, MFE and Normalized Bayesian Information Criterion (NBIC).…”

Section: Model Selectionmentioning

confidence: 99%

“…Figure 5 on the other hand, indicates the forecasted value from 1985 to 2035. Czerwinski et al (2007) explained that the forecasted and actual values need to be very close, meaning that the forecasting error must be very low for the model to qualify as good. As observed in Table 4, the noise residues were a combination of positive and negative errors indicating that the model had a good performance of forecasting.…”

Section: Forecastingmentioning

confidence: 99%

See 2 more Smart Citations

Application of stochastic models in predicting Lake Malawi water levels

Makwinja¹,

Phiri²,

Kosamu³

et al. 2017

Int. J. Water Res. Environ. Eng.

View full text Add to dashboard Cite

Stochastic models have proven to be practically fundamental in fields such as science, economics, and business, among others. In Malawi, stochastic models have been used in fisheries to forecast fish catches. Nevertheless, forecasting water levels in major lakes and rivers in Malawi has been given little attention despite the availability of ample historical data. Although previous multichannel seismic surveys revealed the presence of low stands (sediment bypass zone) in Lake Malawi indicating that since the beginning of its formation, important water level fluctuations have been occurring, these previous surveys failed to predict and highlight much more clearly the status of these levels in the future. Therefore, the main objective of the study was to fill these research gaps. The study used

show abstract

Section: Model Selectionmentioning

confidence: 96%

Section: Model Selectionmentioning

confidence: 99%

Section: Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Application of stochastic models in predicting Lake Malawi water levels

Makwinja¹,

Phiri²,

Kosamu³

et al. 2017

Int. J. Water Res. Environ. Eng.

View full text Add to dashboard Cite

show abstract

“…Since time series are currently employed in different and various fields of knowledge-telecommunications [34], fisheries [35], medicine [36], etc.-it is important to perform a script that allows to give a global and integrated vision on the treatment of time series grouping all the relevant information with the characteristics of the series and prediction models.…”

Section: Applicationmentioning

confidence: 99%

Development of a Modelling Script of Time Series Suitable for Data Mining

Sanz‐Fernández¹,

Cabrera²,

Muñoz‐Lechuga³

et al. 2016

OJS

View full text Add to dashboard Cite

Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, economics, etc.). In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis (time-series), free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others. Therefore, ARIMA models appear as a Data Mining technique, offering reliable, robust and high-quality results, to help validate and sustain the research carried out.

show abstract

“…The ANNs characteristics have favoured the use of such methodologies for the development of applications related to fishery resources planning and management (Freón et al, 2003;HardmanMountford et al, 2003;Huse and Ottersen, 2003;Maravelias et al, 2003;Hyun et al, 2005;Chen and Hare, 2006;Czerwinski et al, 2007;Gutiérrez-Estrada et al, 2007;Velo-Suárez and Gutiérrez-Estrada, 2007).…”

Section: Introductionmentioning

confidence: 99%

Hook selectivity models assessment for black spot seabream. Classic and heuristic approaches

Czerwinski

Gutiérrez‐Estrada

Casimiro-Soriguer-Escofet

et al. 2010

Fisheries Research

View full text Add to dashboard Cite

a b s t r a c tSize selectivity of the deep water longline used in the black spot seabream (Pagellus bogaraveo) fishery in the Strait of Gibraltar was studied with data of four sizes of hooks. Logistic (classic) and Artificial Neural Networks (heuristic) selectivity models were fitted for two experimental fishing trials. Logistic selectivity model was adequate for only one of the two periods analysed and the inferior results obtained with the classical approach were significantly improved by ANNs. These results indicate that in the event that the classic models do not fit well, perhaps due to poor quality of the data (such as a smaller sample size or highly overlapped distributions), the simpler ANNs models, with capacity to combine linear relationships and highly non-linear, are most appropriate to establish the functional relation between variables.

show abstract

Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches

Cited by 39 publications

References 32 publications

Application of stochastic models in predicting Lake Malawi water levels

Application of stochastic models in predicting Lake Malawi water levels

Development of a Modelling Script of Time Series Suitable for Data Mining

Hook selectivity models assessment for black spot seabream. Classic and heuristic approaches

Contact Info

Product

Resources

About