Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2011.6033650
Title: Hybrid model incorporating multiple scale dynamics for time series forecasting
Authors: Sharma, V.
Srinivasan, D. 
Keywords: Evolutionary Strategies
Excitable System
FHN Coupled System
Mean Reversion
Multi-Regime behavior
Issue Date: 2011
Source: Sharma, V.,Srinivasan, D. (2011). Hybrid model incorporating multiple scale dynamics for time series forecasting. Proceedings of the International Joint Conference on Neural Networks : 3235-3242. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2011.6033650
Abstract: Most of the real world physical systems have critical thresholds, also known as tipping points, at which the system abruptly shifts its state from one to another. From dynamical system's perspective, bifurcation is the phenomenon responsible for these critical transitions in the system. There are various directions which can be adopted to study this bifurcation problem in an attempt to predict this phenomenon. The focus of this paper is classical bifurcation theory based approach incorporating multiple scale dynamics which is able to give analysis of bifurcations responsible for critical transitions in electricity price time series system. Fitz-Hugh Nagumo (FHN), which is a classical example exhibiting slow-fast scale dynamics is studied and later on hybridized with nonlinear neural networks to model this time series in various markets. Encouraging results allow us to look into this approach in future. © 2011 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/70499
ISBN: 9781457710865
DOI: 10.1109/IJCNN.2011.6033650
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