Advances in Time Series Forecasting

CONTENTS
PREFACE
List of Contributors
Fuzzy Time Series Forecasting Models Evaluation Based on A Novel Distance Measure
	Cagdas Hakan Aladag1,* and I. Burhan Turksen2
	INTRODUCTION
	THE PROPOSED DISTANCE MEASURE AND THE SUGGESTED PERFORMANCE CRITERION
	THE APPLICATION
	CONCLUDING REMARKS
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
A New Fuzzy Time Series Forecasting Model with Neural Network Structure
	Eren Bas* and Erol Egrioglu
	INTRODUCTION
	PROPOSED METHOD
	APPLICATION
	CONCLUSIONS AND DISCUSSIONS
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
Two Factors High Order Non Singleton Type-1 and Interval Type-2 Fuzzy Systems for Forecasting Time Series with Genetic Algorithm
	M.H. Fazel Zarandi1, *, M. Yalinezhaad1 and I.B. Turksen2
	INTRODUCTION
		Interval Type-2 Fuzzy Logic Sets and Systems
		Type-2 Fuzzy Logic Sets
		Non Singleton Interval Type-2 Fuzzy Logic Systems
		Determination of Footprints of Uncertainty (Umf and Lmf) in Interval Type-2 Fuzzy Logic Sets
		Fundamental Concepts of Fuzzy Time Series
		Proposed Two Factors High Order Non Singletontype-1 and Interval Type-2 Fuzzy Time Series Systems
		Tuning Method for Type-1 and Interval Type-2 FTSs with Genetic Algorithm
		Experimental Results by Temperature Prediction and TAIEX Forecasting
			Temperature Prediction with Proposed Method
		TAIEX Forecasting By Applying the Proposed Method with Genetic Algorithm
		GA Procedure
		Selection and Pairing
		Crossover
		Mutation and Reinsertion
		Termination Condition
		Type Reduction and Defuzzification
	CONCLUSION AND FUTURE WORKS
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
A New Neural Network Model with Deterministic Trend and Seasonality Components for Time Series Forecasting
	Erol Egrioglu1,*, Cagdas Hakan Aladag2, Ufuk Yolcu3, Eren Bas1 and Ali Z. Dalar1
	INTRODUCTION
	CLASSICAL TIME SERIES FORECASTING MODELS
	ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES
	A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS
	APPLICATIONS
	CONCLUSION
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process
	Ozge Cagcag Yolcu*
	INTRODUCTION
	FUZZY TIME SERIES
	RELATED METHODS
		Genetic Algorithm (GA)
		Single Multiplicative Neuron Model
	PROPOSED METHOD
	APPLICATIONS
	CONCLUSION AND DISCUSSION
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix
	Cagdas Hakan Aladag1,* and Hilal Guney2
	INTRODUCTION
	FUZZY TIME SERIES
	TSAUR ‘S FUZZY TIME SERIES MARKOV CHAIN MODEL
	THE IMPLEMENTATION
	CONCLUSION
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
A New High Order Multivariate Fuzzy Time Series Forecasting Model
	Ufuk Yolcu*
	INTRODUCTION
	RELATED METHODOLOGY
		The Fuzzy C-Means (FCM) Clustering Method
		Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN)
		Fuzzy Time Series
	THE PROPOSED METHOD
	APPLICATIONS
	CONCLUSIONS AND DISCUSSION
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
Fuzzy Functions Approach for Time Series Forecasting
	Ali Z. Dalar1,*, Erol Egrioglu1, Ufuk Yolcu2 and Cagdas Hakan Aladag3
	INTRODUCTION
	TYPE-1 FUZZY FUNCTIONS APPROACH
	IMPLEMENTATION
		Australian Beer Consumption Time Series
		Turkey Electricity Consumption Time Series
	CONCLUSIONS
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
Recurrent ANFIS for Time Series Forecasting
	Busenur Sarıca1,*, Erol Eğrioğlu2 and Barış Aşıkgil3
	INTRODUCTION
	RECURRENT ADAPTIVE NETWORK FUZZY INFERENCE SYSTEMS
	APPLICATION
	CONCLUSION
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
A Hybrid Method for Forecasting of Fuzzy Time Series
	Eren Bas*
	INTRODUCTION
	THE METHODS USED IN THIS STUDY
		Fuzzy Time Series
		Genetic Algorithm
		Differential Evolution Algorithm
	PROPOSED METHOD
	APPLICATION
		Analysis of Canadian Lynx Data
	CONCLUSIONS
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENTS
	REFERENCES
SUBJECT INDEX