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發(fā)表于 2025-3-21 17:02:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Grammar-Based Feature Generation for Time-Series Prediction
編輯Anthony Mihirana De Silva,Philip H. W. Leong
視頻videohttp://file.papertrans.cn/388/387799/387799.mp4
叢書(shū)名稱(chēng)SpringerBriefs in Applied Sciences and Technology
圖書(shū)封面Titlebook: ;
出版日期Book 2015
版次1
doihttps://doi.org/10.1007/978-981-287-411-5
isbn_softcover978-981-287-410-8
isbn_ebook978-981-287-411-5Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
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https://doi.org/10.1007/978-3-322-93453-6 as finance, energy, signal processing, astronomy, resource management and economics. Time-series prediction attempts to predict future events/behaviour based on historical data. In this endeavour, it is a considerable challenge to capture inherent nonlinear and non-stationary characteristics presen
板凳
發(fā)表于 2025-3-22 02:27:11 | 只看該作者
,Diagnose Krebs – was hei?t das eigentlich?,vant features while at the same time speeding up the learning task. Given . features, the FS problem is to find the optimal subset among . possible choices. This problem quickly becomes intractable as . increases. In the literature, suboptimal approaches based on sequential and random searches using
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Introduction, as finance, energy, signal processing, astronomy, resource management and economics. Time-series prediction attempts to predict future events/behaviour based on historical data. In this endeavour, it is a considerable challenge to capture inherent nonlinear and non-stationary characteristics presen
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