{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T01:10:09Z","timestamp":1750986609627,"version":"3.41.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"S5","license":[{"start":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T00:00:00Z","timestamp":1510617600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61300095","11604094"],"award-info":[{"award-number":["61300095","11604094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2017JJ2068"],"award-info":[{"award-number":["2017JJ2068"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science Research Foundation of Hunan Provincial Education Department","award":["16C0479"],"award-info":[{"award-number":["16C0479"]}]},{"name":"Science Research Foundation of Hunan Provincial Education Department","award":["14A037"],"award-info":[{"award-number":["14A037"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s10586-017-1316-3","type":"journal-article","created":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T14:51:31Z","timestamp":1510671091000},"page":"11129-11141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The power load\u2019s signal analysis and short-term prediction based on wavelet decomposition"],"prefix":"10.1007","volume":"22","author":[{"given":"Huan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruishi","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,11,14]]},"reference":[{"issue":"1","key":"1316_CR1","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10586-016-0708-0","volume":"20","author":"DG Hu","year":"2017","unstructured":"Hu, D.G., Shu, H., Hu, H.D.: Spatiotemporal regression Kriging to predict precipitation using time-series MODIS data. Cluster Comput. 20(1), 347\u2013357 (2017). SI","journal-title":"Cluster Comput."},{"issue":"1","key":"1316_CR2","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s10586-017-0803-x","volume":"20","author":"GW Zhang","year":"2017","unstructured":"Zhang, G.W., Xu, L.Y., Xue, Y.L.: Model and forecast stock market behavior integrating investor sentiment analysis and transaction data. Cluster Comput. 20(1), 789\u2013803 (2017)","journal-title":"Cluster Comput."},{"key":"1316_CR3","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.solener.2017.04.020","volume":"149","author":"Y Hirata","year":"2017","unstructured":"Hirata, Y., Aihara, K.: Improving time series prediction of solar irradiance after sunrise: comparison among three methods for time series prediction. Solar Energy 149, 294\u2013301 (2017)","journal-title":"Solar Energy"},{"key":"1316_CR4","doi-asserted-by":"crossref","unstructured":"Yang, G.L., Cao, S.Q., Wu, Y.: Recent advancements in signal processing and machine learning. Math. Probl. Eng. 2014 Article ID 549024 (2014)","DOI":"10.1155\/2014\/549024"},{"issue":"B3","key":"1316_CR5","doi-asserted-by":"publisher","first-page":"5003","DOI":"10.1029\/1998JB900106","volume":"104","author":"F Moreau","year":"1999","unstructured":"Moreau, F., Gibert, D., Holschneider, M., et al.: Identification of sources of potential fields with the continuous wavelet transform: basic theory. J. Geophys. Res. 104(B3), 5003\u20135013 (1999)","journal-title":"J. Geophys. Res."},{"key":"1316_CR6","volume-title":"A Wavelet Tour of Signal Processing: The Sparse Way","author":"S Mallat","year":"2009","unstructured":"Mallat, S.: A Wavelet Tour of Signal Processing: The Sparse Way, vol. 3. Elsevier, Amsterdam (2009)"},{"key":"1316_CR7","doi-asserted-by":"crossref","unstructured":"Kumari, G.S., Kumar, S.k.: Electrocardio graphic signal analysis using wavelet transforms. In: 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), pp. 1\u20136 (2015)","DOI":"10.1109\/EESCO.2015.7253688"},{"issue":"1\u20132","key":"1316_CR8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/0167-2789(93)90009-P","volume":"65","author":"MT Rosenstein","year":"1993","unstructured":"Rosenstein, M.T., Collins, J.J., Luca, C.J.D.: A practical method for calculating largest Lyapunov exponents from small data sets. Phys. D 65(1\u20132), 117\u2013134 (1993)","journal-title":"Phys. D"},{"key":"1316_CR9","doi-asserted-by":"crossref","unstructured":"Yong, Z.: New prediction of chaotic time series based on local Lyapunov exponent. Chin. Phys. Lett. 22(5) Article ID 020503 (2013)","DOI":"10.1088\/1674-1056\/22\/5\/050502"},{"issue":"3","key":"1316_CR10","first-page":"273","volume":"20","author":"C Corts","year":"1995","unstructured":"Corts, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"1316_CR11","first-page":"1","volume":"000","author":"V Petra","year":"2017","unstructured":"Petra, V., Anna, B.E., Viera, R., Slavom\u00edr, \u0160., et al.: Smart grid load forecasting using online support vector regression. Comput. Electr. Eng. 000, 1\u201316 (2017)","journal-title":"Comput. Electr. Eng."},{"key":"1316_CR12","doi-asserted-by":"crossref","unstructured":"Su, L.Y., Li, C.L.: Local prediction of chaotic time series based on polynomial coefficient autoregressive model. Math. Probl. Eng. 2015, Article ID 901807","DOI":"10.1155\/2015\/901807"},{"key":"1316_CR13","doi-asserted-by":"crossref","unstructured":"Qu, J.L., Wang, X.F., Qiao, Y.C, et al.: An improved local weighted linear prediction model for chaotic time series. Chin. Phys. Lett. 31(2) Article ID 020503 (2014)","DOI":"10.1088\/0256-307X\/31\/2\/020503"},{"key":"1316_CR14","doi-asserted-by":"crossref","unstructured":"Frandes, M., Timar, B., Timar, R., Lungeanu, D.: Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models. Sci. Rep. 7 Article ID 6232 (2017)","DOI":"10.1038\/s41598-017-06478-4"},{"issue":"1","key":"1316_CR15","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s41066-015-0003-0","volume":"1","author":"L Livi","year":"2016","unstructured":"Livi, L., Sadeghian, A.: Granular computing, computational intelligence, and the analysis of non-geometric input spaces. Granul. Comput. 1(1), 13\u201320 (2016)","journal-title":"Granul. Comput."},{"issue":"1","key":"1316_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s41066-015-0004-z","volume":"1","author":"M Antonelli","year":"2016","unstructured":"Antonelli, M., Ducange, P., Lazzerini, B., Marcelloni, F.: Multi-objective evolutionary design of granular rule-based classifiers. Granul. Comput. 1(1), 37\u201358 (2016)","journal-title":"Granul. Comput."},{"issue":"1","key":"1316_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s41066-015-0007-9","volume":"1","author":"P Lingras","year":"2016","unstructured":"Lingras, P., Haider, F., Triff, M.: Granular meta-clustering based on hierarchical, network, and temporal connections. Granul. Comput. 1(1), 71\u201392 (2016)","journal-title":"Granul. Comput."},{"issue":"2","key":"1316_CR18","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s41066-015-0002-1","volume":"1","author":"A Skowron","year":"2016","unstructured":"Skowron, A., Jankowski, A., Dutta, S.: Interactive granular computing. Granul. Comput. 1(2), 95\u2013113 (2016)","journal-title":"Granul. Comput."},{"issue":"2","key":"1316_CR19","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s41066-015-0008-8","volume":"1","author":"D Dubois","year":"2016","unstructured":"Dubois, D., Prade, H.: Bridging gaps between several forms of granular computing. Granul. Comput. 1(2), 115\u2013126 (2016)","journal-title":"Granul. Comput."},{"issue":"2","key":"1316_CR20","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/s41066-015-0011-0","volume":"1","author":"Y Yao","year":"2016","unstructured":"Yao, Y.: A triarchic theory of granular computing. Granul. Comput. 1(2), 145\u2013157 (2016)","journal-title":"Granul. Comput."},{"issue":"3","key":"1316_CR21","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s41066-015-0013-y","volume":"1","author":"D Ciucci","year":"2016","unstructured":"Ciucci, D.: Orthopairs and granular computing. Granul. Comput. 1(3), 159\u2013170 (2016)","journal-title":"Granul. Comput."},{"key":"1316_CR22","first-page":"67","volume":"315","author":"S Mallat","year":"1989","unstructured":"Mallat, S.: Multi-resolution approximations and wavelet orthogonal bases of l2(r). Trans. Am. Math. Soc. 315, 67\u201387 (1989)","journal-title":"Trans. Am. Math. Soc."},{"key":"1316_CR23","volume-title":"A Tutorial on Support Vector Regression","author":"A Smola","year":"1998","unstructured":"Smola, A., Scholkopf, B.: A Tutorial on Support Vector Regression. Royal Holloway College, London (1998)"},{"issue":"3","key":"1316_CR24","first-page":"2006","volume":"12","author":"HR Zhang","year":"2003","unstructured":"Zhang, H.R., Han, Z.Z.: An improved sequential minimal optimization learning algorithm for regression support vector machine. J. Softw. 12(3), 2006\u20132013 (2003)","journal-title":"J. Softw."},{"key":"1316_CR25","volume-title":"Detecting Strange Attractors in Fluid Turbulence","author":"F Takens","year":"1981","unstructured":"Takens, F.: Detecting Strange Attractors in Fluid Turbulence. Springer, Berlin (1981)"},{"key":"1316_CR26","doi-asserted-by":"crossref","unstructured":"Gautama, T., Mandic, D.P., Van Hulle, M.M.: A differential entropy based method for determining the optimal embedding parameters of a signal. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing. Hong Kong, China: IEEE, pp. 29\u201332 (2003)","DOI":"10.1109\/ICASSP.2003.1201610"},{"key":"1316_CR27","first-page":"1137","volume":"14","author":"R Kohavi","year":"1995","unstructured":"Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Int. Joint Conf. Artif. Intell. 14, 1137\u20131143 (1995)","journal-title":"Int. Joint Conf. Artif. Intell."},{"issue":"1","key":"1316_CR28","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.jspi.2003.10.004","volume":"128","author":"CR Rao","year":"2005","unstructured":"Rao, C.R., Wu, Y.: Linear model selection by cross-validation. J. Stat. Plan. Inference 128(1), 231\u2013240 (2005)","journal-title":"J. Stat. Plan. Inference"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-017-1316-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10586-017-1316-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-017-1316-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:41:02Z","timestamp":1750984862000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10586-017-1316-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,11,14]]},"references-count":28,"journal-issue":{"issue":"S5","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["1316"],"URL":"https:\/\/doi.org\/10.1007\/s10586-017-1316-3","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2017,11,14]]},"assertion":[{"value":"19 August 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2017","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2017","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2017","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}