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.
The 42 checked references that resolve
resolves10.1016/j.asoc.2017.01.015Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
resolves10.3390/su8040387Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price
resolves10.1109/59.99410A regression-based approach to short-term system load forecasting
resolves10.2307/1912773Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
resolves10.1016/j.swevo.2017.05.003Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms
resolves10.1016/j.asoc.2017.04.014Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network
resolves10.1016/S0957-4174(00)00027-0Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index
resolves10.1109/MCI.2015.2471235Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
resolves10.1098/rspa.1998.0193The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
resolves10.1016/j.renene.2012.06.012A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks
resolves10.1109/SMC.2016.7844431Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network
resolves10.1137/0515056Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape
resolves10.1109/3477.740166A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction
resolves10.1016/j.eswa.2010.10.027Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange
resolves10.1016/j.asoc.2014.12.028A 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
checked 2026-08-09 — re-checked daily as this page is visited;
titles and statuses come from Crossref and DataCite and are not part of the signed record
Both snippets point at the live badge image and link back to this page. The
badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.