Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Inductive functional programming using incremental program transformation
Artificial Intelligence
Data structures and genetic programming
Advances in genetic programming
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Programming and Evolvable Machines
Some Considerations on the Reason for Bloat
Genetic Programming and Evolvable Machines
Generality and Difficulty in Genetic Programming: Evolving a Sort
Proceedings of the 5th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Fighting Bloat with Nonparametric Parsimony Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Reducing Bloat in Genetic Programming
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Genetic Programming Prediction of Solar Activity
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Seeding Genetic Programming Populations
Proceedings of the European Conference on Genetic Programming
Genetic Programming Bloat with Dynamic Fitness
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Backwarding: An Overfitting Control for Genetic Programming in a Remote Sensing Application
Selected Papers from the 5th European Conference on Artificial Evolution
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Computational Statistics & Data Analysis - Special issue: Computational econometrics
A long memory property of stock market returns and a new model
Essays in econometrics
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Overfitting avoidance in genetic programming of polynomials
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Methodology for long-term prediction of time series
Neurocomputing
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
A numerical approach to genetic programming for system identification
Evolutionary Computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Dynamics of genetic programming and chaotic time series prediction
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Generality versus size in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Regularization approach to inductive genetic programming
IEEE Transactions on Evolutionary Computation
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model
IEEE Transactions on Evolutionary Computation
Symbolic regression of multiple-time-scale dynamical systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterized by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on many-step-ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods' advantages in long memory time series forecasting.