Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors

Guo, Yan; Tang, Dezhao; Tang, Wei; Yang, Senqi; Tang, Qichao; Feng, Yang; Zhang, Fang

doi:10.3390/su141710483

Cited by 20 publications

(11 citation statements)

References 56 publications

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“…In comparison to stacked models and multiple regression, model performance is lower at predicting values, as seen by higher MAPE values approximately 11% observed of SVR for 15 days of average prediction. Most of the researchers have focused LSTM (long short-term memory) (2,4) , Neural network (20) , which needs a huge amount of data as compared to the meta-model presented in the current study, and Traditional statistical approaches like ARIMA, SARIMAX (3,5,11) , which required high computing capacity of classical Machine learning models such as autoregressive models, Feature engineering is performed manually and not able to learn more complex data patterns ultimately. Almost all the published research work reported improvement in accuracy with Bi -Direction LSTM.…”

Section: Resultsmentioning

confidence: 99%

“…An increase in differential order will take more time to run models like ARIMA, SARIMAX. (2)(3)(4)(5)11,20) and Logarithmic Complexity increases with the SARIMAX, Same doesn't apply to LSTM.…”

Section: Resultsmentioning

confidence: 99%

“…The most important factor in agricultural sustainable development, which strongly influences the economy, is the cost of agricultural produce. (2) The key issue is that rural Indian farmers still don't have access to the newest technologies, making it challenging for them to obtain the information they need and causing price swings. Despite the pandemic we are currently facing, the agriculture sector rose by 1.2% in January 2021 compared to the same month in 2020, and small farmers have been performing a vital role in the Agri -sector's recovery (3) .…”

Section: Related Methodologymentioning

confidence: 99%

See 2 more Smart Citations

Stacked Ensembles: Boosting Model Performance to New Heights Based on Regression for Forecasting Future Wheat Commodities Prices in Gujarat

Joshi¹,

Patel²

2022

IJST

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Objectives:The goal of this study is to develop an estimation method that will improve absolute and proportionate price predictions, helping farmers in their long-term efforts to boost output and profit. Methods: For the experimentation, Dataset is made up of wheat commodities price of different mandi's of Gujarat region which is collected from agmarknet website, run by Indian government and weather variables which are obtained from weather API (world weather online website) based on commodities region such as Max-Min Temp, Pressure, Wind, WindSpeed, Humidity respectively which has a direct impact on crop production. Various machine learning approaches such as SVR (Support vector regressor), DTR (Decision tree regression), PR(Polynomial regression), Multiple regression, and Boosting algorithms such as XgBoost, Light GBM experimented for Daily data, 10 days and 15 days average price estimation, from the experimentation, observed that DTR Can't be performed well with continuous numerical data. The presence of one or two outliers in the data can seriously affect the results of PR (sensitive to the outliers). The meta-learner attempts to minimize the weakness and maximize the strengths of every individual model such as Multiple regression and SVR (Support vector regressor). Findings: The development of a stacking model, which aids a farmer in more accurately predicting crop price, is motivated by the study of several agriculture-specific requirements like weather parameters, and fuel cost. Every individual regression model like the Decision tree regressor, Support vector regressor, and polynomial regressor is evaluated by the meta-learner to identify its strengths and weaknesses. Novelty: This research addresses the complexity of crop price forecasting along with unique feature set which includes (i) historical commodities prices (ii) weather data (iii) transportation factors iiii) Holiday Impacts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Stacked Ensembles: Boosting Model Performance to New Heights Based on Regression for Forecasting Future Wheat Commodities Prices in Gujarat

Joshi¹,

Patel²

2022

IJST

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“…The three models are implemented on the preprocessed data and evaluated based on the values obtained by comparing metrics and their accuracy in predicting future sales [12]. The performance of each model is compared using metrics such as MAE, RMSE, and MAPE.…”

Section: Objectivementioning

confidence: 99%

Food price index prediction using time series models: A study of Cereals, Millets and Pulses

Majhi,

Bano,

Srichan

et al. 2023

Preprint

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The prediction of household food price index has always been a significant challenge for the food industry, especially in developing countries like India, where the majority of the population depends on agriculture for their livelihoods. In this project, we aim to develop a food price index prediction system for household food items like cereals, millets, and pulses using three popular time-series forecasting models, namely SARIMA, ETS, and FB Prophet. We use historical price index data to build and evaluate the forecasting models. The performance of each method is assessed using evaluation metrics such as MAE and RMSE. The results show that all three methods can effectively predict the demand for food items with high accuracy. However, FB Prophet has better performance than the other two methods when it comes to forecasting accuracy and computation time. This project presents a food prediction model that can be used by grocery stores and households to effectively plan and manage their food inventory. The study highlights the effectiveness of time series forecasting techniques such as SARIMA, ETS, and FB Prophet in predicting the demand for household food items, which can aid in reducing food wastage and improving food supply chain management The developed forecasting model can help retailers and suppliers to manage their inventory and plan their production based on the predicted demand for household food items. Additionally, this study provides valuable insights into the application of time series forecasting methods in the food industry.

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“…That is, a feature can only fuse the information contained in the two pieces of data before and after it, but not the information of the data separated from it directly. In this paper, we combined ERNIE-Gram and TextGCN for modeling [ 22 , 23 , 24 ]. Feature extraction was performed using ERNIE-Gram for Chinese, taking into full consideration the relationship between coarse-grained and fine-grained data.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Yang

Duan

Xiao

et al. 2022

IJERPH

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Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.

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Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors

Cited by 20 publications

References 56 publications

Stacked Ensembles: Boosting Model Performance to New Heights Based on Regression for Forecasting Future Wheat Commodities Prices in Gujarat

Stacked Ensembles: Boosting Model Performance to New Heights Based on Regression for Forecasting Future Wheat Commodities Prices in Gujarat

Food price index prediction using time series models: A study of Cereals, Millets and Pulses

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

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