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Fusion of multiple indicators with ensemble incremental learning techniques for stock price forecasting

https://doi.org/10.1007/s42786-018-00006-2
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42/42 checkable references clean · checked 2026-08-09

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

14 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 42 checked references that resolve
resolves10.1016/j.asoc.2017.01.015
Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
resolves10.1016/j.inffus.2016.11.006
Fusion of multiple diverse predictors in stock market
resolves10.3390/su8040387
Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price
resolves10.1016/j.ins.2015.11.039
Random vector functional link network for short-term electricity load demand forecasting
resolves10.1016/j.ijforecast.2003.09.015
Forecasting seasonals and trends by exponentially weighted moving averages
resolves10.1109/59.99410
A regression-based approach to short-term system load forecasting
resolves10.2307/1912773
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
resolves10.1016/0304-4076(86)90063-1
Generalized autoregressive conditional heteroskedasticity
resolves10.1016/j.asoc.2012.09.024
Support vector regression with chaos-based firefly algorithm for stock market price forecasting
resolves10.1023/A:1017181826899
Glossary of Terms
resolves10.1016/j.swevo.2017.05.003
Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms
resolves10.1016/j.asoc.2017.04.014
Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network
resolves10.1016/S0169-2070(99)00045-X
Forecasting the short-term demand for electricity
resolves10.1023/A:1010933404324
Random Forests
resolves10.1016/S0957-4174(00)00027-0
Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index
resolves10.1016/j.asoc.2008.08.001
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
resolves10.1007/978-1-4757-2440-0
The Nature of Statistical Learning Theory
resolves10.1016/j.eswa.2010.08.004
A multiple-kernel support vector regression approach for stock market price forecasting
resolves10.1126/science.1127647
Reducing the Dimensionality of Data with Neural Networks
resolves10.1162/neco.1997.9.8.1735
Long Short-Term Memory
resolves10.1109/ICIS.2016.7550882
Deep learning for stock prediction using numerical and textual information
resolves10.1080/00207179208934315
Neural-net computing and the intelligent control of systems
resolves10.1016/0925-2312(94)90053-1
Learning and generalization characteristics of the random vector functional-link net
resolves10.1109/ICPR.1992.201708
Feedforward neural networks with random weights
resolves10.1016/j.ins.2015.09.025
A comprehensive evaluation of random vector functional link networks
resolves10.1109/MCI.2015.2471235
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
resolves10.1109/CIEL.2014.7015739
Ensemble deep learning for regression and time series forecasting
resolves10.1109/TPWRS.2012.2197639
Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering
resolves10.1016/j.ijepes.2012.09.002
A hybrid intelligent algorithm based short-term load forecasting approach
resolves10.1098/rspa.1998.0193
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
resolves10.1016/j.renene.2012.06.012
A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks
resolves10.1109/SMC.2016.7844431
Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network
resolves10.1109/SSCI.2015.105
Detecting Wind Power Ramp with Random Vector Functional Link (RVFL) Network
resolves10.1137/0515056
Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape
resolves10.1016/j.renene.2016.02.054
Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition
resolves10.1556/ComEc.5.2004.1.3
An introduction to wavelet analysis with applications to vegetation time series
resolves10.1109/3477.740166
A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction
resolves10.1016/j.eswa.2010.10.027
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange
resolves10.1142/S1793536909000047
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
resolves10.1080/01621459.1937.10503522
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
resolves10.1016/j.eswa.2014.10.031
Predicting stock market index using fusion of machine learning techniques
resolves10.1016/j.asoc.2014.12.028
A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price
The 14 references without a DOI — listed, not checked
no DOI — not checkedBox GEP, Jenkins G (1990) Time series analysis, forecasting and control. Holden-Day Inc, San Francisco. ISBN 0816211043
no DOI — not checkedCortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
no DOI — not checkedKrizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems. Curran Associates, New York, pp 1097–1105
no DOI — not checkedDing X, Zhang Y, Liu T, Duan J (2015) Deep learning for event-driven stock prediction. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence (IJCAI 2015), AAAI Press, pp 2327–2333
no DOI — not checkedDietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems. Lecture notes in computer science, vol 1857. Springer, Berlin, Heidelberg
no DOI — not checkedBreiman L (1996) Stacked regressions. Mach Learn 24:49–64
no DOI — not checkedCormen TH, Leiserson CE, Rivest RL, Stein C (2000) Introduction to algorithms. MIT Press, Cambridge
no DOI — not checkedHaykin S (1999) Neural networks: a comprehensive foundation, International edn. Prentice Hall, Upper Saddle River
no DOI — not checkedYe L, Liu P (2011) Combined model based on EMD-SVM for short-term wind power prediction. In: Proceedings of Chinese society for electrical engineering (CSEE), vol 31, pp 102–108
no DOI — not checkedPercival D, Walden A (2006) Wavelet methods for time series analysis, Cambridge series in statistical and probabilistic mathematics. Cambridge University Press, Cambridge
no DOI — not checkedChen Y, Feng MQ (2003) A technique to improve the empirical mode decomposition in the hilbert-huang transform. Earthq Eng Eng Vib 2:796–808
no DOI — not checkedYahoo finance (2017). http://www.finance.yahoo.com/ . Accessed Sept 2017
no DOI — not checkedNemenyi P (1963) Distribution-free multiple comparisons. Princeton University, Princeton
no DOI — not checkedDemšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
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