Basic statistics of weekly price.
<div><p>Livestock product prices serve as a barometer and bellwether for the agricultural market. However, traditional point prediction techniques focus mainly on tracking or fitting, resulting in limited information and challenges in evaluating the uncertainty of future prices. A compre...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2025
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| _version_ | 1852022695084949504 |
|---|---|
| author | Weimin Ma (3586310) |
| author2 | Lingling Peng (1372977) Hu Chen (19500) Haisheng Yan (20727224) |
| author2_role | author author author |
| author_facet | Weimin Ma (3586310) Lingling Peng (1372977) Hu Chen (19500) Haisheng Yan (20727224) |
| author_role | author |
| dc.creator.none.fl_str_mv | Weimin Ma (3586310) Lingling Peng (1372977) Hu Chen (19500) Haisheng Yan (20727224) |
| dc.date.none.fl_str_mv | 2025-02-14T18:31:37Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0318823.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Basic_statistics_of_weekly_price_/28419926 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three main steps three groups according livestock product based different embedding dimensions achieve high accuracy price fluctuation range weekly price data interval prediction capabilities interval price prediction price prediction interval predictions data composition prediction step prediction algorithms point prediction upper bound term memory results indicate original signal lstm ). lower bound long short limited information joint point incorporating discussions including point fuzzy mathematics fuzzy entropy future prices empirical study decomposition techniques characteristics categorization better characterize agricultural market |
| dc.title.none.fl_str_mv | Basic statistics of weekly price. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Livestock product prices serve as a barometer and bellwether for the agricultural market. However, traditional point prediction techniques focus mainly on tracking or fitting, resulting in limited information and challenges in evaluating the uncertainty of future prices. A comprehensive livestock price prediction model with joint point and interval prediction capabilities is proposed, with fuzzy mathematics and long short-term memory. Three main steps are taken: (1) data composition and reconstruction, to extract a set of relatively stationary subsequence components by complementary ensemble empirical mode decomposition (CEEMD) from original signal, and divide these components into three groups according to fuzzy entropy (FE) value. (2) characteristics categorization, determining the lower bound, mean, and upper bound of the rebuilt data via fuzzy information granulation (FIG) to better characterize the price fluctuation range. (3) price prediction, including point and interval predictions with attention mechanism long short-term memory (AM-LSTM). An empirical study was conducted on the weekly price data of pork, beef, and mutton in China from 2009 to 2023, incorporating discussions on different embedding dimensions, prediction step, fuzzy granulation window sizes, decomposition techniques, and prediction algorithms. The results indicate that the proposed interval prediction model can not only achieve high accuracy in point prediction, but also better capture price change intervals.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_bc3902b67b3c4e022dccac5845f759da |
| identifier_str_mv | 10.1371/journal.pone.0318823.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28419926 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Basic statistics of weekly price.Weimin Ma (3586310)Lingling Peng (1372977)Hu Chen (19500)Haisheng Yan (20727224)Science PolicyPlant BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthree main stepsthree groups accordinglivestock product baseddifferent embedding dimensionsachieve high accuracyprice fluctuation rangeweekly price datainterval prediction capabilitiesinterval price predictionprice predictioninterval predictionsdata compositionprediction stepprediction algorithmspoint predictionupper boundterm memoryresults indicateoriginal signallstm ).lower boundlong shortlimited informationjoint pointincorporating discussionsincluding pointfuzzy mathematicsfuzzy entropyfuture pricesempirical studydecomposition techniquescharacteristics categorizationbetter characterizeagricultural market<div><p>Livestock product prices serve as a barometer and bellwether for the agricultural market. However, traditional point prediction techniques focus mainly on tracking or fitting, resulting in limited information and challenges in evaluating the uncertainty of future prices. A comprehensive livestock price prediction model with joint point and interval prediction capabilities is proposed, with fuzzy mathematics and long short-term memory. Three main steps are taken: (1) data composition and reconstruction, to extract a set of relatively stationary subsequence components by complementary ensemble empirical mode decomposition (CEEMD) from original signal, and divide these components into three groups according to fuzzy entropy (FE) value. (2) characteristics categorization, determining the lower bound, mean, and upper bound of the rebuilt data via fuzzy information granulation (FIG) to better characterize the price fluctuation range. (3) price prediction, including point and interval predictions with attention mechanism long short-term memory (AM-LSTM). An empirical study was conducted on the weekly price data of pork, beef, and mutton in China from 2009 to 2023, incorporating discussions on different embedding dimensions, prediction step, fuzzy granulation window sizes, decomposition techniques, and prediction algorithms. The results indicate that the proposed interval prediction model can not only achieve high accuracy in point prediction, but also better capture price change intervals.</p></div>2025-02-14T18:31:37ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0318823.t001https://figshare.com/articles/dataset/Basic_statistics_of_weekly_price_/28419926CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284199262025-02-14T18:31:37Z |
| spellingShingle | Basic statistics of weekly price. Weimin Ma (3586310) Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three main steps three groups according livestock product based different embedding dimensions achieve high accuracy price fluctuation range weekly price data interval prediction capabilities interval price prediction price prediction interval predictions data composition prediction step prediction algorithms point prediction upper bound term memory results indicate original signal lstm ). lower bound long short limited information joint point incorporating discussions including point fuzzy mathematics fuzzy entropy future prices empirical study decomposition techniques characteristics categorization better characterize agricultural market |
| status_str | publishedVersion |
| title | Basic statistics of weekly price. |
| title_full | Basic statistics of weekly price. |
| title_fullStr | Basic statistics of weekly price. |
| title_full_unstemmed | Basic statistics of weekly price. |
| title_short | Basic statistics of weekly price. |
| title_sort | Basic statistics of weekly price. |
| topic | Science Policy Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified three main steps three groups according livestock product based different embedding dimensions achieve high accuracy price fluctuation range weekly price data interval prediction capabilities interval price prediction price prediction interval predictions data composition prediction step prediction algorithms point prediction upper bound term memory results indicate original signal lstm ). lower bound long short limited information joint point incorporating discussions including point fuzzy mathematics fuzzy entropy future prices empirical study decomposition techniques characteristics categorization better characterize agricultural market |