Long Short-Term Memory Model Based Agriculture Commodity Price Prediction Application

Chen, Zhiyuan; Ling, Sin Kai

doi:10.1145/3417473.3417481

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…The primary function of the prediction network is to predict the future motion of human. Considering the periodicity of lower limb movement, the proposed algorithm implements the long short-term memory (LSTM) [ 37 , 38 ] to accomplish the goal. The prediction network consists of two independent parts corresponding to the angle and event prediction.…”

Section: Methodsmentioning

confidence: 99%

Online Adaptive Prediction of Human Motion Intention Based on sEMG

Ding

Yang

Wang

et al. 2021

Sensors

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Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

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Section: Methodsmentioning

confidence: 99%

Online Adaptive Prediction of Human Motion Intention Based on sEMG

Ding

Yang

Wang

et al. 2021

Sensors

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“…Five machine learning methods were compared in this research, which are ARIMA [20], SVR [21], Prophet [22], XGBoost [23] and LSTM [17]. Technical details of these methods have been presented in our conference paper [1]. Finally, process 4 selects the best model out of all models to serve as the prediction engine.…”

Section: Prediction Engine Designmentioning

confidence: 99%

“…According to the lowest error percentage, many researchers have selected ANN and PLS as prediction algorithms. --Long Short-Term Memory (LSTM) Chen [1] Chicken, Chili, Tomato ARIMA, SVR, Prophet, XGBoost and LSTM Arun Kumar [9] proposed a system to apply prediction by analyzing past soil and rainfall datasets. In another research paper, Askunuri Manjula [10] has done crop prediction using multiple features, such as weather forecasting, pesticides and fertilizers and past revenue.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques

Chen¹,

Goh²,

Sin³

et al. 2021

Adv. sci. technol. eng. syst. j.

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The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper machine learning techniques for automated agriculture commodity price prediction still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges. In this research, we address these problems by conducting a machine learning strategy study and propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia. The results validate the efficiency of the proposed Long Short-Term Memory Model (LSTM) to serve as the prediction engine for the proposed system. Particularly in the long-term experiment testing, the average performance of LSTM with MSE has improved 45.5% while ARIMA has dropped 74.1% and the average MSE of LSTM is 0.304 which outperformed all other four algorithms.

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“…Theoretical aspects of the functioning of food markets, in particular their features of functioning are discussed in the works of such scientists as T. Horodetska [19], Ya. Beznesiuk [20], N. Hurzhii [21], O. Krasnorutskyi [22], O. Kravchenko [23], M. Ginanneschi [24], N. Shelest [25], O. Sopotsko [26], C. Yuan and S. Ling [27], V. Gallego et al [28], R. Ramón et al [29]. However, the variety of approaches to determining the strategic directions of effective development of the sales market indicates the unresolved issues of the industry, in particular in the regional aspect and the state's influence on the creation of innovation and investment environment in the processing industry of meat processing complex.…”

Section: Introductionmentioning

confidence: 99%