A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing

Wu, Haoyang; Wu, Huaili; Zhu, Min; Chen, Weifeng; Chen, Wei

doi:10.1186/s40537-016-0062-3

Cited by 63 publications

(16 citation statements)

References 22 publications

(21 reference statements)

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“…were analyzed to mine useful rules for appropriate cultivation. Wu et al [61] proposed a mixed model, which combined the ARIMA model and regression method based on time and space factors, and produced warnings for daily price changes using neural networks.…”

Section: Combined Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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Section: Combined Methodsmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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“…By doing a series of transformations, the DWT could be formalized as one scaling function ∅ (5) and one discrete wavelet function (6). Furthermore, the DWT coefficients of ( ) can be acquired through 7and (8).…”

Section: Dwtmentioning

confidence: 99%

“…Support vector regression (SVR) is applied to overcome LR's shortcomings for power consumption forecasting by using different nonlinear kernels in [7]. However, the SVR-based method is easier to be overfitting when data is broad [8]. Time series-based methods aim at finding the relationship between future consumption values and past values.…”

Section: Introductionmentioning

confidence: 99%

Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting

et al. 2020

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Short-term power consumption forecasting plays a critical role in the process of building the smart grid. However, it is very challenging as the power consumption series has strong randomness and volatility. In this paper, the authors proposed a novel domain fusion deep model based on convolutional neural network (CNN), long short-term memory (LSTM), and discrete wavelet transform (DWT) to deal with this task accurately. The proposed deep model has two channels: raw power consumption and DWT. The raw power consumption channel corresponding to time-domain feature extraction while the DWT channel is frequency domain. They extract time-domain and frequency-domain features individually by using CNN. CNN extracted time-domain, and frequency-domain features are merged as time-frequency fusion features, which fully reflect the changing power consumption trend. The time-frequency fusion features are fed into LSTM to mining the features which have a long-time dependency. The comprehensive features are the fusion of time-domain and frequency-domain features with a long-time dependency, which are utilized for power consumption forecasting. The proposed method is evaluated on two public nature data sets related to power consumption with multiple metrics. The comparative experimental analysis has confirmed the state-of-theart performance of the proposed method for short-term power consumption forecasting.

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“…One frequently used transfer learning method is TrAda-Boost [27], which generates predictions for a new environment by modifying the relative weights of data across settings at each step of a boosting algorithm, so as to identify and utilize data from those settings that are most similar to the new environment [28]. The effective application of transfer learning methods often requires a large amount of data [26,29,30]. Furthermore, the purely data-driven perspective of transfer learning ignores lurking variables and the insights on AM processes that can be obtained from them [26,31].…”

Section: Empiricalmentioning

confidence: 99%

Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems

Francis

Sabbaghi

Shankar

et al. 2020

Journal of Manufacturing Science and Engineering

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Distortion in laser-based additive manufacturing (LBAM) is a critical issue that adversely affects the geometric integrity of additively manufactured parts and generally exhibits a complicated dependence on the underlying material. The differences in properties between distinct materials prevent the immediate application of a distortion model learned for one material to another, which introduces the challenge in LBAM of learning a distortion model for a new material system given past experiments. Current methods for investigating the distortion of different material systems typically involve finite element analysis or a large number of experiments in an empirical study. However, these methods do not learn from previous experiments and can incur significant costs in terms of computation, time, or resources. We propose a Bayesian model transfer methodology that is both physics-based and data-driven to leverage past experiments on previously studied material systems for more efficient distortion modeling of new systems. This method transfers distortion models across distinct materials based on the statistical effect equivalence framework by formulating the differences between two materials as a lurking variable. Our method reduces the experimentation and effort needed for specifying distortion models for new material systems. We validate our methodology in a case study of distortion model transfer from Ti–6Al–4V disks to 316L stainless steel disks. This case study is the first instance of model transfer between material systems and illustrates the ability of the Bayesian model transfer methodology to address the issue of comprehensive distortion modeling across varying material systems in LBAM.

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A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing

Cited by 63 publications

References 22 publications

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting

Efficient Distortion Prediction of Additively Manufactured Parts Using Bayesian Model Transfer Between Material Systems

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