@article{(Open Science Index):https://publications.waset.org/pdf/7488,
	  title     = {Adaptive Neuro-Fuzzy Inference System for Financial Trading using Intraday Seasonality Observation Model},
	  author    = {A. Kablan},
	  country	= {},
	  institution	= {},
	  abstract     = {The prediction of financial time series is a very
complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather
controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends
the Adaptive Neuro Fuzzy Inference System for High Frequency
Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high
frequency. However, in order to eliminate unnecessary input in the
training phase a new event based volatility model was proposed.
Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based
volatility model provides the ANFIS system with more accurate input
and has increased the overall performance of the system.},
	    journal   = {International Journal of Economics and Management Engineering},
	  volume    = {3},
	  number    = {10},
	  year      = {2009},
	  pages     = {1909 - 1918},
	  ee        = {https://publications.waset.org/pdf/7488},
	  url   	= {https://publications.waset.org/vol/34},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 34, 2009},