Abstract
One potential strength of recurrent neural networks (RNNs) is their – theoretical – ability to find a connection between cause and consequence in time series in an constraint-free manner, that is without the use of explicit probability theory. In this work we present a solution which uses the echo state approach for this purpose. Our approach learns probabilities explicitly using an online learning procedure and echo state networks. We also demonstrate the approach using a test model.
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References
Boedecker, J., Obst, O., Mayer, N.M., Asada, M.: Initialization and self-organized optimization of recurrent neural network connectivity. HFSP J. 3(5), 340–349 (2009)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)
Hammer, B., Schrauwen, B., Steil, J.J.: Recent advances in efficient learning of recurrent networks. In: ESANN 2009 proceedings, European Symposium on Artificial Neural Networks, pp. 213–226 (2009)
Jaeger, H.: The ‘echo state’ approach to analysing and training recurrent neural networks. GMD Report 148, GMD German National Research Insitute for Computer Science (2001)
Jaeger, H.: Short term memory in echo state networks. GMD Report 152, GMD - German National Research Institute for Computer Science (2002)
Jaeger, H.: Adaptive nonlineaer systems identification with echo state networks. In: Advances in Neural Information Processing Systems, pp. 609–615 (2003)
Pereda, E., Quiroga, R.Q., Bhattacharya, J.: Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology (77), 1–37 (2005)
Schreiber, T.: Measuring information transfer. Physical Review Letters 85(2), 461–464 (2000)
Shibuya, T., Harada, T., Kuniyoshi, Y.: Causality quantification and its applications: structuring and modeling of multivariate time series. In: KDD 2009: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 787–796. ACM, New York (2009)
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Mayer, N.M., Obst, O., Yu-Chen, C. (2010). Time Series Causality Inference Using Echo State Networks. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_35
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DOI: https://doi.org/10.1007/978-3-642-15995-4_35
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