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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kraemer, Klaus | |
dc.contributor.author | J. Green, Tim | |
dc.contributor.author | D. Karakochuk, Crystal | |
dc.contributor.author | C. Whitfield, Kyly | |
dc.date.accessioned | 2022-05-03T09:53:00Z | - |
dc.date.available | 2022-05-03T09:53:00Z | - |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9781498756792 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/147363 | - |
dc.language.iso | eng | |
dc.publisher | Taylor & Francis | |
dc.relation.isbasedon | 10.1201/crcoxistrdis | |
dc.relation.uri | https://library.oapen.org/bitstream/20.500.12657/30632/1/644634.pdf | |
dc.rights.uri | CC BY-NC-ND (姓名標示-非商業性-禁止改作) | |
dc.source | DOAB | |
dc.subject.classification | Medicine | |
dc.subject.other | short-term load forecasting | |
dc.subject.other | weighted k-nearest neighbor (W-K-NN) algorithm | |
dc.subject.other | comparative analysis | |
dc.subject.other | empirical mode decomposition (EMD) | |
dc.subject.other | particle swarm optimization (PSO) algorithm | |
dc.subject.other | intrinsic mode function (IMF) | |
dc.subject.other | support vector regression (SVR) | |
dc.subject.other | short term load forecasting | |
dc.subject.other | crude oil price forecasting | |
dc.subject.other | time series forecasting | |
dc.subject.other | hybrid model | |
dc.subject.other | complementary ensemble empirical mode decomposition (CEEMD) | |
dc.subject.other | sparse Bayesian learning (SBL) | |
dc.subject.other | multi-step wind speed prediction | |
dc.subject.other | Ensemble Empirical Mode Decomposition | |
dc.subject.other | Long Short Term Memory | |
dc.subject.other | General Regression Neural Network | |
dc.subject.other | Brain Storm Optimization | |
dc.subject.other | substation project cost forecasting model | |
dc.subject.other | feature selection | |
dc.subject.other | data inconsistency rate | |
dc.subject.other | modified fruit fly optimization algorithm | |
dc.subject.other | deep convolutional neural network | |
dc.subject.other | multi-objective grey wolf optimizer | |
dc.subject.other | long short-term memory | |
dc.subject.other | fuzzy time series | |
dc.subject.other | LEM2 | |
dc.subject.other | combination forecasting | |
dc.subject.other | wind speed | |
dc.subject.other | electrical power load | |
dc.subject.other | crude oil prices | |
dc.subject.other | time series forecasting | |
dc.subject.other | improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) | |
dc.subject.other | kernel learning | |
dc.subject.other | kernel ridge regression | |
dc.subject.other | differential evolution (DE) | |
dc.subject.other | artificial intelligence techniques | |
dc.subject.other | energy forecasting | |
dc.subject.other | condition-based maintenance | |
dc.subject.other | asset management | |
dc.subject.other | renewable energy consumption | |
dc.subject.other | Gaussian processes regression | |
dc.subject.other | state transition algorithm | |
dc.subject.other | five-year project | |
dc.subject.other | forecasting | |
dc.subject.other | Markov-switching | |
dc.subject.other | Markov-switching GARCH | |
dc.subject.other | energy futures | |
dc.subject.other | commodities | |
dc.subject.other | portfolio management | |
dc.subject.other | active investment | |
dc.subject.other | diversification | |
dc.subject.other | institutional investors | |
dc.subject.other | energy price hedging | |
dc.subject.other | metamodel | |
dc.subject.other | ensemble | |
dc.subject.other | individual | |
dc.subject.other | regression | |
dc.subject.other | interpolation | |
dc.title | The biology of the first 1000 days | |
dc.type | 電子教科書 | |
dc.classification | 醫學類 | |
Theme: | 教科書-醫學類 |
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