{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:21:33Z","timestamp":1772119293748,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71971089"],"award-info":[{"award-number":["71971089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72001083"],"award-info":[{"award-number":["72001083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00500-023-09121-9","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T13:02:30Z","timestamp":1692882150000},"page":"14673-14688","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Time series to imaging-based deep learning model for detecting abnormal fluctuation in agriculture product price"],"prefix":"10.1007","volume":"27","author":[{"given":"Wentao","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Dabin","family":"zhang","sequence":"additional","affiliation":[]},{"given":"Liwen","family":"Ling","sequence":"additional","affiliation":[]},{"given":"Guotao","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Lling","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"9121_CR1","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.matpr.2019.10.051","volume":"21","author":"T Adithiyaa","year":"2020","unstructured":"Adithiyaa T, Chandramohan D, Sathish T (2020) Optimal prediction of process parameters by GWO-KNN in stirring-squeeze casting of AA2219 reinforced metal matrix composites[J]. Mater Today Proceed 21:1000\u20131007","journal-title":"Mater Today Proceed"},{"key":"9121_CR2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1111\/j.1467-9892.2005.00392.x","volume":"26","author":"F Battaglia","year":"2005","unstructured":"Battaglia F, Orfei L (2005) Outlier detection and estimation in nonlinear time series. J Time Ser Anal 26:107\u2013121","journal-title":"J Time Ser Anal"},{"issue":"2","key":"9121_CR3","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1198\/106186002760180536","volume":"11","author":"C Bilen","year":"2002","unstructured":"Bilen C, Huzurbazar S (2002) Wavelet-based detection of outliers in time series. J Comput Graph Statist 11(2):311\u2013327","journal-title":"J Comput Graph Statist"},{"issue":"3","key":"9121_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381028","volume":"53","author":"A Boukerche","year":"2020","unstructured":"Boukerche A, Zheng L, Alfandi O (2020) Outlier detection: Methods, models, and classification. ACM Computing Surveys (CSUR) 53(3):1\u201337","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"8","key":"9121_CR5","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1371\/journal.pone.0023378","volume":"6","author":"AS Campanharo","year":"2011","unstructured":"Campanharo AS, Sirer MI, Malmgren RD, Ramos FM, Amaral LAN (2011) Duality between time series and networks. PloS One 6(8):233\u2013248","journal-title":"PloS One"},{"key":"9121_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102076","volume":"62","author":"J Chakraborty","year":"2020","unstructured":"Chakraborty J, Nandy A (2020) Discrete wavelet transform based data representation in deep neural network for gait abnormality detection. Biomed Signal Process Control 62:102076","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"9121_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1080\/00401706.1988.10488367","volume":"30","author":"I Chang","year":"1988","unstructured":"Chang I, Chen TC (1988) Estimation of time series parameters in the presence of outliers. Technometrics 30(2):193\u2013204","journal-title":"Technometrics"},{"key":"9121_CR8","doi-asserted-by":"crossref","unstructured":"Charyyev B, Gunes MH (2020) Detecting anomalous IoT traffic flow with locality sensitive hashes[C]\/\/GLOBECOM 2020-2020 IEEE Global Communications Conference. IEEE: 1\u20136","DOI":"10.1109\/GLOBECOM42002.2020.9322559"},{"issue":"421","key":"9121_CR9","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1080\/01621459.1993.10594321","volume":"88","author":"C Chen","year":"1993","unstructured":"Chen C, Liu LM (1993) Joint estimation of model parameters and outlier effects in time series. J Am Statist Assoc 88(421):284\u2013297","journal-title":"J Am Statist Assoc"},{"key":"9121_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2020.106960","volume":"149","author":"W Dai","year":"2020","unstructured":"Dai W, Mrkvika T, Sun Y et al (2020) Functional outlier detection and taxonomy by sequential transformations. Comput Statist Data Analysis 149:106960","journal-title":"Comput Statist Data Analysis"},{"issue":"8","key":"9121_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/ac15cc","volume":"16","author":"FM Davenport","year":"2021","unstructured":"Davenport FM, Shukla S, Turner W et al (2021) Sending out an SOS: using start of rainy season indicators for market price forecasting to support famine early warning[J]. Environm Res Lett 16(8):084050","journal-title":"Environm Res Lett"},{"key":"9121_CR12","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.patrec.2020.12.010","volume":"143","author":"N Dey","year":"2021","unstructured":"Dey N, Zhang YD, Rajinikanth V et al (2021) Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recogn Lett 143:67\u201374","journal-title":"Pattern Recogn Lett"},{"key":"9121_CR13","unstructured":"Frieda R, Agueusopa I, Bornkampb B et al (2012)\u201cBayesian outlier detection in INGARCH time series,\u201d Sonderforschungsbereich (SFB) 823"},{"key":"9121_CR14","doi-asserted-by":"publisher","first-page":"16843","DOI":"10.1007\/s00521-018-03970-4","volume":"32","author":"Y Ge","year":"2019","unstructured":"Ge Y, Wu H (2019) Prediction of corn price fluctuation based on multiple linear regression analysis model under big data. Neural Comput Appl 32:16843\u201316855","journal-title":"Neural Comput Appl"},{"issue":"11","key":"9121_CR15","doi-asserted-by":"publisher","first-page":"2580","DOI":"10.1016\/j.csda.2009.12.010","volume":"54","author":"A Graneand","year":"2010","unstructured":"Graneand A, Veiga H (2010) Wavelet-based detection of outliers in financial time series. Comput Statist Data Analysis 54(11):2580\u20132593","journal-title":"Comput Statist Data Analysis"},{"issue":"5","key":"9121_CR16","first-page":"243","volume":"33","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. IEEE Computer Soc 33(5):243\u2013249","journal-title":"IEEE Computer Soc"},{"issue":"2","key":"9121_CR17","first-page":"229","volume":"26","author":"GK Jha","year":"2013","unstructured":"Jha GK, Sinha K (2013) Agricultural price forecasting using neural network model: an innovative information delivery system. Agric Econ Res 26(2):229\u2013239","journal-title":"Agric Econ Res"},{"issue":"5","key":"9121_CR18","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.1007\/s11063-022-10783-z","volume":"54","author":"W Jiang","year":"2022","unstructured":"Jiang W, Zhang D, Ling L et al (2022) Time series classification based on image transformation using feature fusion strategy. Neural Process Lett 54(5):3727\u20133748","journal-title":"Neural Process Lett"},{"key":"9121_CR19","doi-asserted-by":"crossref","unstructured":"Jiang W, Ling L, Zhang D, et al (2023) A time series forecasting model selection framework using CNN and data augmentation for small sample data. Neural Process Lett 1\u201328","DOI":"10.1007\/s11063-022-11113-z"},{"key":"9121_CR20","doi-asserted-by":"crossref","unstructured":"Keogh E, Lin J, Fu A (2005) \u201cHOT SAX: efficiently finding the most unusual time series subsequence,\u201d in Proceedings of the 5th IEEE International Conference on Data Mining (ICDM \u201905), pp. 226-233, Houston, Tex, USA, November","DOI":"10.1109\/ICDM.2005.79"},{"issue":"1","key":"9121_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s40745-021-00344-x","volume":"10","author":"A Kurani","year":"2023","unstructured":"Kurani A, Doshi P, Vakharia A et al (2023) A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting[J]. Annals Data Sci 10(1):183\u2013208","journal-title":"Annals Data Sci"},{"key":"9121_CR22","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1111\/j.1467-9892.2010.00688.x","volume":"32","author":"CL Leduca","year":"2011","unstructured":"Leduca CL, Boistardb H, Moulinesc E, Taqqu MS, Reisene VA (2011) Robust estimation of the scale and of the auto covariance function of Gaussian short and long-range dependent processes. J Time Ser Anal 32:135\u2013156","journal-title":"J Time Ser Anal"},{"key":"9121_CR23","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/s00170-019-03557-w","volume":"103","author":"Z Li","year":"2019","unstructured":"Li Z, Li J, Wang Y et al (2019) A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. Int J Adv Manufactur Technol 103:499\u2013510","journal-title":"Int J Adv Manufactur Technol"},{"key":"9121_CR24","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1080\/1351847X.2019.1647864","volume":"26","author":"N Loperfido","year":"2020","unstructured":"Loperfido N (2020) Kurtosis-based projection pursuit for outlier detection in financial time series. Eur J Finance 26:142\u2013164","journal-title":"Eur J Finance"},{"issue":"1","key":"9121_CR25","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1093\/mnras\/stw656","volume":"459","author":"RJ Lyon","year":"2016","unstructured":"Lyon RJ et al (2016) Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach. Monthly Notices Royal Astron Soc 459(1):1104\u20131123","journal-title":"Monthly Notices Royal Astron Soc"},{"key":"9121_CR26","doi-asserted-by":"crossref","unstructured":"Madaan Lovish (2019) Price Forecasting & Anomaly Detection for Agricultural Commodities in India. Assoc Comput Machine","DOI":"10.1145\/3314344.3332488"},{"issue":"8","key":"9121_CR27","doi-asserted-by":"publisher","first-page":"2511","DOI":"10.1016\/j.jspi.2008.12.014","volume":"139","author":"FF Molinares","year":"2009","unstructured":"Molinares FF, Reisen VA, Cribari-Neto F (2009) Robust estimation in long-memory processes under additive outliers. J Statist Plann Inference 139(8):2511\u20132525","journal-title":"J Statist Plann Inference"},{"key":"9121_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12920-020-0677-2","volume":"13","author":"M Mostavi","year":"2020","unstructured":"Mostavi M, Chiu YC, Huang Y et al (2020) Convolutional neural network models for cancer type prediction based on gene expression. BMC Med Genom 13:1\u201313","journal-title":"BMC Med Genom"},{"issue":"2","key":"9121_CR29","first-page":"106","volume":"20","author":"B Mutavd\u017ei\u0107","year":"2016","unstructured":"Mutavd\u017ei\u0107 B, Novkovi\u0107 N, Vukeli\u0107 N et al (2016) Analysis and prediction of prices and price partyes of corn and wheat in Serbia[J]. J Process Energy Agric 20(2):106\u2013108","journal-title":"J Process Energy Agric"},{"key":"9121_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2020.102282","volume":"57","author":"HD Nguyen","year":"2021","unstructured":"Nguyen HD, Tran KP, Thomassey S et al (2021) Forecasting and anomaly detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management[J]. Int J Inform Manag 57:102282","journal-title":"Int J Inform Manag"},{"key":"9121_CR31","doi-asserted-by":"crossref","unstructured":"Paul RK, Garai S (2021) Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices[J]. Soft Comput 25(20):12857\u201312873","DOI":"10.1007\/s00500-021-06087-4"},{"issue":"10","key":"9121_CR32","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"X Qiu","year":"2020","unstructured":"Qiu X, Sun T, Xu Y et al (2020) Pre-trained models for natural language processing: A survey. Sci China Technol Sci 63(10):1872\u20131897","journal-title":"Sci China Technol Sci"},{"key":"9121_CR33","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.oceaneng.2019.04.024","volume":"182","author":"H Rong","year":"2019","unstructured":"Rong H, Teixeira AP, Soares CG (2019) Ship trajectory uncertainty prediction based on a Gaussian Process model. Ocean Eng 182:499\u2013511","journal-title":"Ocean Eng"},{"key":"9121_CR34","unstructured":"Sanchez-Gonzalez A, Godwin J, Pfaff T, et al (2020) Learning to simulate complex physics with graph networks. Int Conference Machine Learn PMLR: 8459\u20138468"},{"issue":"10","key":"9121_CR35","doi-asserted-by":"publisher","first-page":"2582","DOI":"10.1016\/S2095-3119(20)63368-8","volume":"19","author":"XU Shi-Wei","year":"2020","unstructured":"Shi-Wei XU, Wang Y, Wang SW et al (2020) Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information[J]. J Integrat Agric 19(10):2582\u20132596","journal-title":"J Integrat Agric"},{"key":"9121_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/for.3980070102","volume":"7","author":"RS Tsay","year":"1988","unstructured":"Tsay RS (1988) Outliers, level shifts, and variance changes in time series. Forecasting 7:1\u201320","journal-title":"Forecasting"},{"key":"9121_CR37","doi-asserted-by":"crossref","unstructured":"T\u00fcys\u00fcz F, Yldz N (2020) A novel multi-criteria analysis model for the performance evaluation of bank regions: an application to Turkish agricultural banking[J]. Soft Comput 24(7):5289\u20135311","DOI":"10.1007\/s00500-019-04279-7"},{"key":"9121_CR38","doi-asserted-by":"crossref","unstructured":"Voort, and Havinga PJM, (2012) Statistics-based outlier detection for wireless sensor networks. Int J Geograph Inform Sci 26(8):1373\u20131392","DOI":"10.1080\/13658816.2012.654493"},{"issue":"4","key":"9121_CR39","volume":"723","author":"I Vorotnikov","year":"2021","unstructured":"Vorotnikov I, Rozanov A, Sidelnikova M et al (2021) Outlier detection of the agricultural time series. IOP Conference Series: Earth Environm Sci 723(4):042070","journal-title":"IOP Conference Series: Earth Environm Sci"},{"key":"9121_CR40","doi-asserted-by":"crossref","unstructured":"Wang X, Zhao T, Liu H, et al (2019) Power consumption predicting and anomaly detection based on long short-term memory neural network[C]\/\/2019 IEEE 4th international conference on cloud computing and big data analysis (ICCCBDA). IEEE: 487\u2013491","DOI":"10.1109\/ICCCBDA.2019.8725704"},{"issue":"7","key":"9121_CR41","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C et al (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235\u20131270","journal-title":"Neural Comput"},{"key":"9121_CR42","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1007\/s10845-021-01768-1","volume":"32","author":"P Zhan","year":"2021","unstructured":"Zhan P, Wang S, Wang J et al (2021) Temporal anomaly detection on IIoT-enabled manufacturing. J Intellig Manufactur 32:1669\u20131678","journal-title":"J Intellig Manufactur"},{"issue":"1","key":"9121_CR43","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aa98a6","volume":"13","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Liu Y, Shibata H et al (2018) Virtual nitrogen factors and nitrogen footprints associated with nitrogen loss and food wastage of China\u2019s main food crops[J]. Environm Res Lett 13(1):014017","journal-title":"Environm Res Lett"},{"key":"9121_CR44","first-page":"837","volume":"7","author":"H Zhao","year":"2020","unstructured":"Zhao H (2020) Futures price prediction of agricultural products based on machine learning[J]. Neural Comput Appl 7:837\u2013850","journal-title":"Neural Comput Appl"},{"key":"9121_CR45","unstructured":"Zhou AW, Wang XH, Liu HT (2015) Vocabulary Correlation Text Clustering Based on HowNet. Microelectron Comput"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-09121-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-09121-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-09121-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T16:41:14Z","timestamp":1729960874000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-09121-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,24]]},"references-count":45,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["9121"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-09121-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-1652363\/v1","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,24]]},"assertion":[{"value":"27 July 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}