Prediction for hog prices based on similar sub-series search and support vector regression

Liu, Yiran; Duan, Qingling; Wang, Dongjie; Zhang, Zhentao; Liu, Chunhong

doi:10.1016/j.compag.2019.01.027

Cited by 44 publications

(28 citation statements)

References 13 publications

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“…Agricultural researchers have used SVM in crop yield estimation and livestock, water, and soil management (Liakos et al 2018), and carcass weight prediction for beef cattle (Alonso, Castañón, and Bahamonde 2013). Liu et al (2019) use SVR in the prediction of hog prices, Jheng, Li, and Lee (2018) use it to predict rice yield, and Huang (2015) uses it to evaluate agricultural project bids; however, the application of SVR is absent from leading agricultural economics journals.…”

Section: Methodsmentioning

confidence: 99%

China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach

Shao¹,

Xiong²,

Li³

et al. 2020

American J Agri Economics

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Section: Methodsmentioning

confidence: 99%

China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach

Shao¹,

Xiong²,

Li³

et al. 2020

American J Agri Economics

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show abstract

“…In Equation (23), it refers to the number of new left gilts in the first month of the year (that is, the 1-month-old gilts that are kept in the new born piglets in each month of the year), which is the pork price in the first year of the month. And in Equation (23), all variables are unknown except pork prices.…”

Section: Estimation Methods For New Left Gilts In Each Month ① Construmentioning

confidence: 99%

Prediction of pork supply via the calculation of pig population based on population prediction model

Zhang

Wang

2020

International Journal of Agricultural and Biological Engineering

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Based on the perspective of pig population system prediction, and accorded to principle of pig months transfer, this paper refers to the modeling principle and method of discrete population quantity prediction model. Then the prediction model of pork supply is derived and established: Firstly, the recursive model of pig population system and estimation model of pork supply was established. Then this study estimated the sum of monthly mortality and culling rate of breeding sows. Furthermore, the method for new left gilts in each month and estimation of breeding sows at each month of age was established. Last, this research established the estimation method model of the initial state of pig population. On this basis, an example calculation is made to predict the monthly pork supply in Heilongjiang Province from January 2016 to March 2018 in the future. The results showed that the prediction model of pork supply based on the prediction of pig population system is an effective perspective to study the forecast of pork supply. In the prediction stage, the prediction accuracy of the number of slaughtered fattened hogs was 96.36% and 97.54%, and the prediction accuracy of pork supply was 98.08% and 93.82%.This study not only lay a theoretical foundation for further study on the balance between pork supply and demand, but also helps to guide pork producers and governments at all levels to make relevant production decisions and plans.

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“…ELM is a single hidden layer feedforward neural networks proposed by [45]. Unlike traditional learning algorithms in feedforward neural network, where parameters are tuned iteratively, the Moore-Penrose generalized inverse is applied to determine the output weights in ELM [6], thus requiring little time for training. This advantage has been applied to classification tasks and regression tasks in numerous studies [27], [46], [47].…”

Section: Forecast Modelmentioning

confidence: 99%

“…Xiong, et al [5] proposed the STL-ELM method for forecasting vegetable prices in China. Liu et al [6] predicted the cyclical and trend components of hog prices using a sub-series search method and SVR. All of these studies consistently report their superiority compared with statistical models.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

et al. 2020

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The fluctuations of agricultural commodity prices have a great impact on people's daily lives as well as the inputs and outputs of agricultural production. An accurate forecast of commodity prices is therefore essential if agricultural authorities are to make scientific decisions. To forecast prices more adaptively, this study proposes a novel model selection framework which includes time series features and forecast horizons. Twenty-nine features are used to depict agricultural commodity prices and three intelligent models are specified as the candidate forecast models; namely, artificial neural network (ANN), support vector regression (SVR), and extreme learning machine (ELM). Both random forest (RF) and support vector machine (SVM) are applied to learn the underlying relationships between the features and the performances of the candidate models. Additionally, a minimum redundancy and maximum relevance approach (MRMR) is employed to reduce feature redundancy and further improve the forecast accuracy. The experimental results demonstrate that, firstly, the proposed model selection framework has a better forecast performance compared with the optimal candidate model and simple model average; secondly, feature reduction is a workable approach to further improve the performance of the model selection framework; and thirdly, for bean and pig grain products, different distributions of the time series features lead to a different selection of the optimal models. INDEX TERMS Model selection, agricultural commodity, price forecasting, time series features, forecast horizons.

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Prediction for hog prices based on similar sub-series search and support vector regression

Cited by 44 publications

References 13 publications

China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach

China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach

Prediction of pork supply via the calculation of pig population based on population prediction model

Forecasting Agricultural Commodity Prices Using Model Selection Framework With Time Series Features and Forecast Horizons

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