[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119498-en":3,"doc-seo-119498-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119498,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","BUILDING PREDICTIVE MODELS OF AGRICULTURAL COMMODITY PRICES BASED ON MACHINE LEARNING METHODS","Agricultural commodity markets are highly volatile, making reliable price forecasting essential for food security and economic decision-making. This study develops predictive models using machine learning algorithms and combines historical price data with climatic variables to uncover patterns that drive market trends. Results show machine learning models outperform traditional statistical approaches, enabling actionable insights for farmers, traders, and policymakers. Further work is recommended to incorporate real-time data and adaptive modeling for rapidly changing market conditions.","РАСШИРЕННЫЙ СИСТЕМНЫЙ АНАЛИЗ И ДИНАМИЧЕСКОЕМОДЕЛИРОВАНИЕ РЫНОЧНЫХ ПРОЦЕССОВ  \nUDC 004.8  \nAdanin Kouakan Arnaud Nicaise,  \nmaster student,  \nEngineering School of Information Technologies, Telecommunications and Control Systems,  \nUral Federal University named after the first President of Russia B.N. Yeltsin  \nYekaterinburg, Russia  \nBalungu Daniel Musafiri,  \npostgraduate,  \nEngineering School of Information Technologies, Telecommunications and Control Systems,  \nUral Federal University named after the first President of Russia B.N. Yeltsin  \nYekaterinburg, Russia  \nBUILDING PREDICTIVE MODELS OF AGRICULTURAL COMMODITY PRICES BASED ON MACHINE LEARNING METHODS  \nAbstract:  \nThe agricultural sector is volatile, requiring robust predictive models to forecast commodity prices. This study uses machine learning methods to build predictive models using various algorithms. The research uses historical price data and climatic data to identify patterns influencing market trends. Machine learning models significantly outperform traditional statistical methods, providing actionable insights for farmers, traders, and policymakers. Future research should explore real-time data integration and adaptive models for dynamic market conditions.  \nKeywords:  \nAgricultural commodity, price prediction, machine learning, time series analysis.  \nThe agricultural sector plays a pivotal role in the global economy, providing food security and livelihoods for millions of people. However, it faces numerous challenges, including price volatility of agricultural commodities, which can significantly impact farmers, consumers, and policymakers. As global demand for food continues to rise due to population growth and changing dietary preferences, accurate prediction of agricultural commodity prices becomes increasingly crucial. This research paper explores the development of predictive models for agricultural commodity prices using advanced machine learning (ML) techniques, aiming to enhance forecasting accuracy and support better decisionmaking in agriculture. Agricultural price prediction is essential for various stakeholders, including farmers, traders, government agencies, and consumers. For farmers, accurate price forecasts can inform planting decisions, optimize resource allocation, and enhance profitability. Traders rely on price predictions to make informed buying and selling decisions, while policymakers use this information to formulate strategies that stabilize markets and ensure food security. Furthermore, consumers benefit from stable prices that reflect true market conditions. Therefore, improving the accuracy of agricultural price predictions can lead to more efficient market operations and better economic outcomes for all involved [1] .  \nDespite its importance, predicting agricultural commodity prices is fraught with challenges. The agricultural market is influenced by a myriad of factors, including weather conditions, supply chain dynamics, geopolitical events, and changes in consumer preferences. These factors contribute to the inherent complexity and non-linearity of price movements. Traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) have been widely used for time series forecasting; however, they often fall short in capturing the complex relationships within the data [2] . As a result, there is a growing interest in applying machine learning techniques that can handle large datasets and uncover hidden patterns.  \nMachine learning offers a promising alternative to traditional statistical methods by leveraging algorithms that can learn from data without being explicitly programmed. Recent advancements in machine learning have demonstrated its potential to improve forecasting accuracy across various domains, including finance and healthcare. In agriculture, machine learning models such as Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Gradient Boosting Machines (GBM) have show","cbCailSH4Xe5aExi","https://ap.wps.com/l/cbCailSH4Xe5aExi","pdf",473457,1,5,"English","en",105,"# Introduction\n## Motivation and importance of price forecasting\n## Challenges in agricultural price prediction\n# Methodology\n## Machine learning vs. traditional statistical methods\n## Deep learning objective and model development\n# Data Description\n## FAOSTAT dataset and time span\n## Product scope and regulatory classification\n# Results and Discussion\n## Model performance illustration","[{\"question\":\"Why are predictive models for agricultural commodity prices important?\",\"answer\":\"Accurate forecasts support better planting, trading, and policy decisions, helping stabilize markets and improve outcomes for farmers, traders, policymakers, and consumers.\"},{\"question\":\"What factors make agricultural price prediction difficult?\",\"answer\":\"Price movements are affected by non-linear interactions among weather, supply chain dynamics, geopolitical events, and changes in consumer preferences, which challenge conventional forecasting.\"},{\"question\":\"How does the study build its machine learning models and what data is used?\",\"answer\":\"The research develops deep learning models using PyTorch and uses historical producer price data from FAOSTAT together with climatic data to identify patterns influencing market trends.\"}]","BUILDING PREDICTIVE MODELS OF AGRICULTURAL COMMODITY PRICES BASED ON MACHINE LEARNING METHODS | 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are predictive models for agricultural commodity prices important?","Question",{"text":75,"@type":76},"Accurate forecasts support better planting, trading, and policy decisions, helping stabilize markets and improve outcomes for farmers, traders, policymakers, and consumers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What factors make agricultural price prediction difficult?",{"text":80,"@type":76},"Price movements are affected by non-linear interactions among weather, supply chain dynamics, geopolitical events, and changes in consumer preferences, which challenge conventional forecasting.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study build its machine learning models and what data is used?",{"text":84,"@type":76},"The research develops deep learning models using PyTorch and uses historical producer price data from FAOSTAT together with climatic data to identify patterns influencing market 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