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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
02/01/2019 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
RODRIGUES, L. S.; REZENDE, S. O.; MOURA, M. F.; MARCACINI, R. M. |
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Afiliação: |
LUCAS S. RODRIGUES, UFMS; SOLANGE O. REZENDE, UFSCar; MARIA FERNANDA MOURA, CNPTIA; RICARDO M. MARCACINI, UFMS. |
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Título: |
Agribusiness time series forecasting using perceptually important events. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
In: LATIN AMERICAN COMPUTING CONFERENCE, 44., 2018, São Paulo. Anais... São Paulo: Mackenzie, 2018. |
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Páginas: |
10 p. |
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Idioma: |
Inglês |
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Notas: |
CLEI 2018. |
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Conteúdo: |
Resumo- Modern agribusiness management incorporates instruments for risk management with the objective of mitigating uncertainties to the producer. In this context, the producer (riskaverse) transfer the risk of price oscillation to companies or individuals that operate in the futures market and who expect to receive a payment (risk premium) for assuming such risk. Defining the adequate strategies for risk management depends on the knowledge about the problem to determine prices ranges in the future. Recent studies demonstrate that time series forecasting can be significantly improved by considering additional inforation about the problem. In particular, besides the historical time series, textual knowledge extracted from the news portals, social networking and other public data sources available in the web may also be used. This paper presents an approach for agribusiness time series forecasting that allows incorporating external knowledge in the form of events extracted from news about agribusiness, without the need to previously label textual information. In this case, periods of significant uptrends and downtrends of time series are automatically identified - known in the literature as perceptually important points (PIP). We extend the concept of PIP to news events, where similar events published with a certain regularity in periods of uptrends and owntrends are selected as perceptually important events to improve time series forecasting models. An experimental evaluation based on price prediction on ten corn futures contracts (derivatives) provides evidence that the proposed approach is promising. MenosResumo- Modern agribusiness management incorporates instruments for risk management with the objective of mitigating uncertainties to the producer. In this context, the producer (riskaverse) transfer the risk of price oscillation to companies or individuals that operate in the futures market and who expect to receive a payment (risk premium) for assuming such risk. Defining the adequate strategies for risk management depends on the knowledge about the problem to determine prices ranges in the future. Recent studies demonstrate that time series forecasting can be significantly improved by considering additional inforation about the problem. In particular, besides the historical time series, textual knowledge extracted from the news portals, social networking and other public data sources available in the web may also be used. This paper presents an approach for agribusiness time series forecasting that allows incorporating external knowledge in the form of events extracted from news about agribusiness, without the need to previously label textual information. In this case, periods of significant uptrends and downtrends of time series are automatically identified - known in the literature as perceptually important points (PIP). We extend the concept of PIP to news events, where similar events published with a certain regularity in periods of uptrends and owntrends are selected as perceptually important events to improve time series forecasting models. An experimental evaluatio... Mostrar Tudo |
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Palavras-Chave: |
Séries temporais. |
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Thesagro: |
Agronegócio. |
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Thesaurus NAL: |
Agribusiness; Risk management. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/189590/1/agribusiness-time.pdf
|
|
Marc: |
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100 1 $aRODRIGUES, L. S.
245 $aAgribusiness time series forecasting using perceptually important events.$h[electronic resource]
260 $aIn: LATIN AMERICAN COMPUTING CONFERENCE, 44., 2018, São Paulo. Anais... São Paulo: Mackenzie$c2018
300 $a10 p.
500 $aCLEI 2018.
520 $aResumo- Modern agribusiness management incorporates instruments for risk management with the objective of mitigating uncertainties to the producer. In this context, the producer (riskaverse) transfer the risk of price oscillation to companies or individuals that operate in the futures market and who expect to receive a payment (risk premium) for assuming such risk. Defining the adequate strategies for risk management depends on the knowledge about the problem to determine prices ranges in the future. Recent studies demonstrate that time series forecasting can be significantly improved by considering additional inforation about the problem. In particular, besides the historical time series, textual knowledge extracted from the news portals, social networking and other public data sources available in the web may also be used. This paper presents an approach for agribusiness time series forecasting that allows incorporating external knowledge in the form of events extracted from news about agribusiness, without the need to previously label textual information. In this case, periods of significant uptrends and downtrends of time series are automatically identified - known in the literature as perceptually important points (PIP). We extend the concept of PIP to news events, where similar events published with a certain regularity in periods of uptrends and owntrends are selected as perceptually important events to improve time series forecasting models. An experimental evaluation based on price prediction on ten corn futures contracts (derivatives) provides evidence that the proposed approach is promising.
650 $aAgribusiness
650 $aRisk management
650 $aAgronegócio
653 $aSéries temporais
700 1 $aREZENDE, S. O.
700 1 $aMOURA, M. F.
700 1 $aMARCACINI, R. M.
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Embrapa Agricultura Digital (CNPTIA) |
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