Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Xueheng Qiu"'
Publikováno v:
Journal of Banking and Financial Technology. 3:33-42
Predicting stock market index is very challenging as financial time series shows highly non-linear and non-stationary patterns. In this paper, an ensemble incremental learning model is presented for stock price forecasting, which is composed of two d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92c5692425d9312c1c816e0ca9192d82
https://doi.org/10.1007/s42786-018-00006-2
https://doi.org/10.1007/s42786-018-00006-2
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030378370
SEMCCO/FANCCO
SEMCCO/FANCCO
Predicting the trend of stock price movement accurately allows investors to maximize their profits from investments. However, due to the complexity of the stock data, classifiers often make errors, which cause the investors to lose money from failed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c3fa604a0bedf8f639796241d5e5261
https://doi.org/10.1007/978-3-030-37838-7_9
https://doi.org/10.1007/978-3-030-37838-7_9
Publikováno v:
Knowledge-Based Systems. 145:182-196
Short-term electric load forecasting plays an important role in the management of modern power systems. Improving the accuracy and efficiency of electric load forecasting can help power utilities design reasonable operational planning which will lead
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0c48024a827d6f2399ea68deea79a1f
https://doi.org/10.1016/j.knosys.2018.01.015
https://doi.org/10.1016/j.knosys.2018.01.015
Publikováno v:
Information Sciences. 420:249-262
Recent studies in Machine Learning indicates that the classifiers most likely to be the bests are the random forests. As an ensemble classifier, random forest combines multiple decision trees to significant decrease the overall variances. Conventiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f405bf1f059f8599a50f3e97c104f531
https://doi.org/10.1016/j.ins.2017.08.060
https://doi.org/10.1016/j.ins.2017.08.060
Publikováno v:
Applied Soft Computing. 54:246-255
Graphical abstractDisplay Omitted HighlightsAn ensemble deep learning method has been proposed for load demand forecasting.The hybrid method composes of Empirical Mode Decomposition and Deep Belief Network.Empirical Mode Decomposition based methods o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03273b3601f858a3b34c3f2dca9c662f
https://doi.org/10.1016/j.asoc.2017.01.015
https://doi.org/10.1016/j.asoc.2017.01.015
Remaining Useful Life Prediction Using Time-Frequency Feature and Multiple Recurrent Neural Networks
Publikováno v:
ETFA
This paper presents a new framework to improve processing time and accuracy for remaining useful life (RUL) prediction of a degrading equipment. Most of the existing machine learning-based RUL prediction approaches are often not robust to noise and o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97520e2e41d142140411c63bd857f74b
https://doi.org/10.1109/etfa.2019.8869017
https://doi.org/10.1109/etfa.2019.8869017
Publikováno v:
SSCI
In this paper, an ensemble incremental learning model composed of Empirical Mode Decomposition (EMD), Random Vector Functional Link network (RVFL) and Incremental RVFL is presented in this work. First of all, EMD is employed to decompose the historic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcdd5a7df0e2534b21c869edd2c78975
https://doi.org/10.1109/ssci.2018.8628724
https://doi.org/10.1109/ssci.2018.8628724
Autor:
Xueheng Qiu
In recent years, time series forecasting has obtained significant academic and industrial interest with its significance in various application fields, including power system related applications (electric load, wind power and solar irradiance foreca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8a103ffc8de8fe9c5a7b5788bd3b510
https://hdl.handle.net/10356/89432
https://hdl.handle.net/10356/89432
Publikováno v:
SSCI
Wind is a clean and renewable energy source with huge potential in power generation. However, due to the intermittent nature of the wind, the power generated by wind farms fluctuates and often has large ramps, which are harmful to the power grid. Thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fad56c2e6c8239a943bc7ea13b717459
https://doi.org/10.1109/ssci.2017.8285421
https://doi.org/10.1109/ssci.2017.8285421
Publikováno v:
SSCI
Decision Tree is a simple but popular machine learning algorithm. Although a single decision tree is not as accurate as other state-of-the-art classifiers, the performance can be significantly improved by combining the predictions of several decision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2ce9ba0c2f9394696bd9701faf83c49
https://doi.org/10.1109/ssci.2017.8285445
https://doi.org/10.1109/ssci.2017.8285445