Modeling and forecasting of egg production in India using time series models

Khatib, Abdullah Mohammad Ghazi Al; Yonar, Harun; Abotaleb, Mostafa; Mishra, Pradeep Kumar; Yonar, Aynur; Karakaya, Kadir; Badr, Amr; Dhaka, Vinti

doi:10.15312/eurasianjvetsci.2021.352

Cited by 6 publications

(2 citation statements)

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“…The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/v14071367/s1, Table S1: Comparison of Application, assumptions, and limitations of forecasting models and Table S2: Forecast values of FMD outbreak episodes during January 2021 to December 2023 [30,[59][60][61][62][63][64][65][66].…”

Section: Supplementary Materialsmentioning

confidence: 99%

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020

Punyapornwithaya

Mishra

Sansamur

et al. 2022

Viruses

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Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state–space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.

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Section: Supplementary Materialsmentioning

confidence: 99%

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020

Punyapornwithaya

Mishra

Sansamur

et al. 2022

Viruses

Self Cite

View full text Add to dashboard Cite

show abstract

“…The daily milk production of cows was predicted using ARIMA ( da Rosa Righi et al, 2020 ). Khatib et al (2021) gathered early egg production in India and forecasted future values using ARIMA. Uzundumlu and Dilli (2022) employed ARIMA to predict the production of chicken meat in the top chicken-producing countries.…”

Section: Introductionmentioning

confidence: 99%