A novel text-based framework for forecasting agricultural futures using massive online news headlines

Li, Minglu; Li, Guowen; Liu, Mingxi; Zhu, Xiaoqian; Wei, Lu

doi:10.1016/j.ijforecast.2020.02.002

Cited by 98 publications

(52 citation statements)

References 33 publications

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“…The impact of energy shocks on US agricultural productivity was investigated byWang and McPhail (2014) whileKoirala et al (2015) explore the non-linear correlations of energy and agricultural prices withAlbulescu et al (2020) exploring the latter issue further, the last two papers using copulas Xiong et al (2015). is an early reference of forecasting agricultural commodity prices whileKyriazi et al (2019),Wang et al (2019a), andLi et al (2020d) consider three novel and completely different approaches on forecasting agricultural prices and agricultural futures returns. L ópez Cabrera and Schulz (2016) explore volatility linkages between energy and agricultural commodity prices and thenTian et al (2017) start a mini-stream on volatility forecasting on agricultural series followed among others by the work ofLuo et al (2019) and ofDegiannakis et al (2020) de Nicola et al (2016).…”

mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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“…[124] As shown in Figure 3, most of the research is evenly distributed between the first two of the fore-mentioned objects of analysis. The sentiment of mainstream media (more specifically headlines) is only analyzed in (Li et al, 2020). Nine out of eleven publications are working with input text in the English language, except for (Li et al, 2020), where the input text is Chinese and (Singh et al, 2019), where English is mixed with Punjabi dialect.…”

Section: Resultsmentioning

confidence: 99%

“…The sentiment of mainstream media (more specifically headlines) is only analyzed in (Li et al, 2020). Nine out of eleven publications are working with input text in the English language, except for (Li et al, 2020), where the input text is Chinese and (Singh et al, 2019), where English is mixed with Punjabi dialect. The most commonly used algorithm for Sentiment analysis in agriculture is Naive Bayes, which also seems to be the most accurate algorithm used by researchers.…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis in Agriculture

Novák

Benda

Šilerová

et al. 2021

AOL

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Sentiment analysis is currently the most actively researched topic in the field of natural language processing, however, despite it being such a powerful tool, it is not very widely used in the agrarian sector. This research focuses on the discovery and analysis of scientific literature related to Sentiment analysis in agriculture, to provide an overview of how and where Sentiment analysis is used in the agrarian sector and which methods are most commonly used. This article also discusses which applications of Sentiment analysis yield the most benefits and suggests a direction for future research.

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“…O Modelo Auto-Regressivo Integrado de Médias Móveis (ARIMA) têm sido uma das abordagens paramétricas mais populares para previsão de séries temporais em diferentes domínios de aplicação. Modelos não paramétricos têm sido propostos e avaliados em relação aos modelos paramétricos (Wang et al, 2017(Wang et al, , 2019Li et al, 2020). Redes Neurais Artificiais (RNA), Aprendizado Profundo (do inglês, Deep Learning) e Vetores de Suporte de Regressão (do inglês, Support Vector Regression(SVR) são exemplos de modelos não paramétricos que usam dados históricos para aprender uma dependência não linear.…”

Section: Introdu ç ãOunclassified

Previsão do preço futuro de commodities agrícolas: um estudo para enriquecer séries temporais

Filho

Marcacini

Rezende

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Products derived from corn and soy are consumed on a large scale in the world. Market price fluctuations have far-reaching effects on grain consumers, criteria and indices. Thus, the forecast of future grain and grain prices has attracted the attention of investments and agribusiness companies. Estimation, forecasting models use time series to predict future values. However, external factors can originate the data in time series, such as political events, improvement patterns and the foreign exchange market. This information is not explicit in time series data and can make it difficult to predict variable values. Textual data extracted from news, forums and social media can be a source of knowledge about external factors and potentially useful for weather forecasting models. Some studies present text mining techniques to combine textual data with time series. However, existing representations have limitations, such as the curse of dimensionality and ineffective attributes. In this sense, this work proposes representations of time series enriched with textual information. The results indicate that the methods used can be an alternative to improve the prediction performance in regression tasks. Resumo: Os produtos derivados do milho e da soja são consumidos em grande escala no mundo. As flutuações dos preços no mercado têm efeitos de longo alcance sobre os consumidores, agricultores e processadores de grãos. Assim, a previsão de preços futuros desses grãos tem atraído significativa atenção dos pesquisadores e empresas do agronegócio. Geralmente, os modelos de previsão usam séries temporais para predizer valores futuros. No entanto, fatores externos podem influenciar os dados em séries temporais, como eventos políticos, crises econômicas e o mercado de câmbio. Essas informações não são explícitas nos dados da série temporal e podem dificultar a previsão dos valores das variáveis. Os dados textuais extraídos de notícias, fóruns e redes sociais podem ser uma fonte de conhecimento sobre fatores externos e potencialmente úteis para modelos de previsão de séries temporais. Alguns estudos apresentam técnicas de mineração de texto para combinar dados textuais com séries temporais. No entanto, as representações existentes apresentam algumas limitações, como a maldição da dimensionalidade e atributos ineficazes. Nesse sentido, este trabalho propõem representações de séries temporais enriquecidas com informações textuais. Os resultados indicam que os métodos utilizados podem ser uma alternativa para melhorar o desempenho de previsão em tarefas de regressão.

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A novel text-based framework for forecasting agricultural futures using massive online news headlines

Cited by 98 publications

References 33 publications

Forecasting: theory and practice

Forecasting: theory and practice

Sentiment Analysis in Agriculture

Previsão do preço futuro de commodities agrícolas: um estudo para enriquecer séries temporais

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