Advances in time series forecasting. Volume 2

Advances in time series forecasting. Volume 2 / edited by Cagdas Hakan Aladag.

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting frameworks are discussed and their application is demonstrated. The second volume of the series includes...

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Bibliographic Details
Other Authors: Aladag, Cagdas Hakan (Editor)
Format: eBook
Language:English
Published: Sharjah, UAE : Bentham Science Publishers, [2017]
Subjects:
Time-series analysis.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Time-series analysis
Online Access:Click for online access
Table of Contents:
  • Intro
  • CONTENTS
  • PREFACE
  • 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.

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