Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models

Tsitsika, Efthymia V.; Maravelias, Christos D.; Haralabous, John

doi:10.1111/j.1444-2906.2007.01426.x

Cited by 43 publications

(26 citation statements)

References 11 publications

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“…This data includes more than 100 months, which is adequate for a proper time-series analysis (Tsitsika et al, 2007).…”

Section: Sarima Modelsmentioning

confidence: 99%

“…Time series analysis of fishery landings plays a vital role in fisheries management and decision making due to its capacity for demonstrating the trends and seasonality patterns of the data (Koutroumanidis et al, 2006;Tsitsika et al, 2007). In the fishery field, time series analysis qualifies for forecasting because it expresses past patterns and projects into the future (Stergiou et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…So far, this method has been successful in describing and forecasting fishery dynamics of broadly different species-significantly, demersal and pelagic species (Stergiou, 1990; Stergiou and Christow, 1996; Stergiou et al, 1997; Tsitsika et al, 2007), squid (Pierce and Boyle, 2003), mackerel (Lloret et al, 2000;Punzón et al, 2004), loliginid and ommastrephid (Georgakarakos et al, 2002(Georgakarakos et al, , 2006. Songkhla Lagoon is one of the two lagoons in the world that has Irrawaddy dolphins, an endangered species.…”

Section: Introductionmentioning

confidence: 99%

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Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

Hue¹

2018

Appl. Ecol. Env. Res.

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Abstract. Time series analysis techniques and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to analyze monthly fish and shrimp catch landing trends recorded for Songkhla shallow lagoon in Thailand (2003Thailand ( -2016. Autocorrelation (AC) and Partial Autocorrelation (PAC) functions were calculated to build seasonal ARIMA models. These models were well-chosen for explaining the time series and forecasting future catch landings. It is found that both fish and shrimp catch landings tend to fluctuate steadily. The fish catch from 2017 to 2020 is steadily increasing on the average catch for the period 2003-2016 by up to 36.06%, while the shrimp catch is decreasing by around 15.47% for the same period. This study demonstrates the importance of undertaking detailed studies of ecological and economic sustainable development to support the comprehensive fisheries management policy for Songkhla Lagoon. The present study shows an effective tool for making accurate forecasts; it also helps in decision making about, and fisheries management of the Songkhla Lagoon.

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“…This data includes more than 100 months, which is adequate for a proper time-series analysis (Tsitsika et al, 2007).…”

Section: Sarima Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

Hue¹

2018

Appl. Ecol. Env. Res.

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“…ARIMA models have been used for modeling and forecasting of fish catches (Venugopalan and Srinath 1998;Tsitsika et al 2007). The forecasting efficiency of ARIMA models were compared with neural network models (Hanson et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Sugarcane Yield of Tamilnadu Using ARIMA Models

Suresh

Priya

2011

Sugar Tech

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This paper attempts forecasting the sugarcane area, production and productivity of Tamilnadu through fitting of univariate Auto Regressive Integrated Moving Average (ARIMA) models. The data on sugarcane area, production and productivity collected from 1950-2007 has been used for present study. ARIMA (1, 1, 1) model is found suitable for sugarcane area and productivity. AR-IMA (2, 1, 2) is found appropriate for modeling sugarcane production. The performances of models are validated by comparing with actual values. Using the models developed, forecast values for sugarcane area, production and productivity are developed for subsequent years.

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“…The model was also used to obtain seasonal forecast of paddy and food grains by Balasubramanian and Dhanavanthan (2002). ARIMA model was used for modelling and forecasting of fish catches by Venugopalan and Srinath (1998) and Tsitsika et al (2007). ARIMA models were also used in forecasting of milk, fat and protein yields of Italian Simmental cows (Maccioitta et al, 2000;.…”

Section: Instability In Marine Products Export From Indiamentioning

confidence: 99%

Growth, instability and forecast of marine products export from India

Das¹,

Kumar

Rani

2016

Indian J. Fish.

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This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India's marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India's marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

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Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models

Cited by 43 publications

References 11 publications

Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

Forecasting Sugarcane Yield of Tamilnadu Using ARIMA Models

Growth, instability and forecast of marine products export from India

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