Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Punyapornwithaya, Veerasak; Jampachaisri, Katechan; Klaharn, Kunnanut; Sansamur, Chalutwan

doi:10.3389/fvets.2021.775114

Cited by 8 publications

(16 citation statements)

References 45 publications

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“…Moreover, our results revealed that the prediction abilities of the ARIMA and NNAR were approximately comparable. This could be due to the fact that the data set contains both linear and non-linear patterns, and, therefore, the strengths of one model may not provide an advantage over another [ 37 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…Further, the forecasted values were compared to the actual ones in the validation set. In addition, error metrics, including mean absolute percentage error (MAPE), mean absolute scale error (MASE), and root mean square error (RMSE), were calculated using functions from the “Metrics” package in order to measure the predictive abilities of the ARIMA and NNAR models [ 42 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

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Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast

Anwar

Lampang

Preyavichyapugdee

et al. 2022

Viruses

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LSD is an important transboundary disease affecting the cattle industry worldwide. The objectives of this study were to determine trends and significant change points, and to forecast the number of LSD outbreak reports in Africa, Europe, and Asia. LSD outbreak report data (January 2005 to January 2022) from the World Organization for Animal Health were analyzed. We determined statistically significant change points in the data using binary segmentation, and forecast the number of LSD reports using auto-regressive moving average (ARIMA) and neural network auto-regressive (NNAR) models. Four significant change points were identified for each continent. The year between the third and fourth change points (2016–2019) in the African data was the period with the highest mean of number of LSD reports. All change points of LSD outbreaks in Europe corresponded with massive outbreaks during 2015–2017. Asia had the highest number of LSD reports in 2019 after the third detected change point in 2018. For the next three years (2022–2024), both ARIMA and NNAR forecast a rise in the number of LSD reports in Africa and a steady number in Europe. However, ARIMA predicts a stable number of outbreaks in Asia, whereas NNAR predicts an increase in 2023–2024. This study provides information that contributes to a better understanding of the epidemiology of LSD.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast

Anwar

Lampang

Preyavichyapugdee

et al. 2022

Viruses

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“…Forecasts for January 2022 to December 2022 were then generated from the training dataset and compared with the actual values within the same period in the test dataset to assess the forecasting model's performance. Second, in an effort to ensure up-to-date and accurate forecasts, it is suggested that the most current data be used for prediction ( 28 , 34 ).Therefore, all time series models were applied to the full dataset to generate forecasts for the following 3 years (January 2023–2025). A schematic representation of the modeling procedure is provided in Figure 1 for clarity.…”

Section: Methodsmentioning

confidence: 99%

“…To ensure the utilization of the most recent data for forecasting, we applied all the final time series models to the full dataset. This approach facilitated the modeling and forecasting of canine rabies cases for the three subsequent years post the last observation data incorporated in this study, as described in ( 28 ).…”

Section: Methodsmentioning

confidence: 99%

Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data

Punyapornwithaya,

Thanapongtharm,

Jainonthee

et al. 2023

Front. Vet. Sci.

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IntroductionRabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attributed to animal-transmitted rabies. For strategic allocation and optimization of resources, sophisticated and accurate forecasting of rabies incidents is imperative. This research aims to determine temporal patterns, and seasonal fluctuations, and project the incidence of canine rabies throughout Thailand, using various time series techniques.MethodsMonthly total laboratory-confirmed rabies cases data from January 2013 to December 2022 (full dataset) were split into the training dataset (January 2013 to December 2021) and the test dataset (January to December 2022). Time series models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), Error Trend Seasonality (ETS), the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and Seasonal and Trend Decomposition using Loess (STL) were used to analyze the training dataset and the full dataset. The forecast values obtained from the time series models applied to the training dataset were compared with the actual values from the test dataset to determine their predictive performance. Furthermore, the forecast projections from January 2023 to December 2025 were generated from models applied to the full dataset.ResultsThe findings revealed a total of 4,678 confirmed canine rabies cases during the study duration, with apparent seasonality in the data. Among the models tested with the test dataset, TBATS exhibited superior predictive accuracy, closely trailed by the SARIMA model. Based on the full dataset, TBATS projections suggest an annual average of approximately 285 canine rabies cases for the years 2023 to 2025, translating to a monthly average of 23 cases (range: 18–30). In contrast, SARIMA projections averaged 277 cases annually (range: 208–214).DiscussionThis research offers a new perspective on disease forecasting through advanced time series methodologies. The results should be taken into consideration when planning and conducting rabies surveillance, prevention, and control activities.

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“…Even though a study of the spatio-temporal pattern of the outbreak in beef cattle farms was carried out ( 23 ), studies on the epidemiology of LSD outbreaks in dairy farms remain limited, allowing several knowledge gaps, particularly on how the LSD outbreaks distributed among dairy farms in terms of space and time. The findings of the study in beef farming areas ( 23 ) may not be extrapolated to dairy farming areas because beef and dairy cattle farms in Thailand are managed differently ( 31 , 32 ). Furthermore, the spatial distributions of beef and dairy farms differ, with the majority of dairy farming areas being densely populated with dairy farms and beef farms being sparsely populated.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal patterns of lumpy skin disease outbreaks in dairy farms in northeastern Thailand

et al. 2022

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In 2021–2022, there were numerous outbreaks of lumpy skin disease (LSD) affecting cattle farms across Thailand. This circumstance was the country's first encounter with an LSD outbreak. Thus, a better understanding of LSD epidemiology is necessary. The aim of this study was to determine the spatio-temporal patterns of the LSD outbreaks in dairy farming areas. Data from LSD outbreak investigations collected from dairy farms in Khon Kean province, northeastern Thailand, were analyzed using spatio-temporal models including space-time permutation, Poisson, and Bernoulli models. LSD outbreaks were found in 133 out of 152 dairy farms from May to July, 2021. The majority of dairy farms (n = 102) were affected by the LSD outbreaks in June. The overall herd attack, morbidity and mortality rates were 87, 31, and 0.9%, respectively. According to the results of all models, the most likely clusters were found in the northern part of the study area. The space-time permutation and Poisson model identified 15 and 6 spatio-temporal outbreak clusters, respectively, while the Bernoulli model detected only one cluster. The most likely clusters from those models cover radii of 1.59, 4.51, and 4.44 km, respectively. All farms included in the cluster identified by the space-time permutation model were also included in the cluster identified by the Poisson model, implying that both models detected the same outbreak area. Furthermore, the study results suggested that farmers who own farms within a one km radius of the LSD outbreak farm should be advised to implement more stringent insect vector control measures to prevent disease spread. This study provides better insights into the spatio-temporal pattern of clusters of LSD in the outbreak area. The findings of this study can support authorities in formulating strategies to prevent and control future outbreaks as well as prioritizing resource allocation to high-risk areas.

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Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Cited by 8 publications

References 45 publications

Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast

Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast

Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data

Spatio-temporal patterns of lumpy skin disease outbreaks in dairy farms in northeastern Thailand

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