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Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies.
“Portfolio Selection”
Harry Markowitz · 1952
Earlier work this paper cites.
“Some methods for classification and analysis of multivariate observations”
J. MacQueen · 1967
Earlier work this paper cites.
“The Number of Factors in Security Returns”
Stephen. Brown · 1989
Earlier work this paper cites.
“Degree of correlation inside a financial market”
Rosario Mantegna · 1997
Earlier work this paper cites.
“Noise Dressing of Financial Correlation Matrices”
Laurent Laloux, Pierre Cizeau, Jean-Philippe Bouchaud and Marc Potters · 1999
Earlier work this paper cites.
“Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series”
Vasiliki Plerou et al · 1999
Earlier work this paper cites.
“Detection of fixed points in spatiotemporal signals by clustering method”
Axel Hutt, Markus Svensen, F Kruggel and R Friedrich · 2000
Earlier work this paper cites.
“An Introduction to Econophysics”
Rosario Mantegna and H. Stanley · 2000
Earlier work this paper cites.
“Algorithms of maximum likelihood data clustering with applications”
Lorenzo Giada and Matteo Marsili · 2002
Earlier work this paper cites.
“Dissecting financial markets: sectors and states”
Matteo Marsili · 2002
Earlier work this paper cites.
“Bayesian analysis of climate change impacts in phenology”
Volker Dose and Annette Menzel · 2004
Earlier work this paper cites.
“When instability makes sense”
Peter Ashwin and Marc Timme · 2005
Earlier work this paper cites.
“Data Structures for Statistical Computing in Python”
Wes McKinney · 2010
Earlier work this paper cites.
“Local normalization. Uncovering correlations in non-stationary financial time series”
Rudi Schäfer and Thomas Guhr · 2010
Earlier work this paper cites.
“Identifying States of a Financial Market”
Michael. Münnix et al · 2012
Earlier work this paper cites.
“A map of the Brazilian stock market”
Leonidas Sandoval · 2012
Earlier work this paper cites.
“Correlation of financial markets in times of crisis”
Leonidas Sandoval and Italo Franca · 2012
Earlier work this paper cites.
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“Electricity Consumption and Economic Growth: Evidence from Poland”
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Wolfgang von Linden, Volker Dose and Udo von Toussaint · 2014
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“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
Martín et al · 2015
“Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead”
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Springer International Publishing, 2019
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