Genetic and Evolutionary Algorithms for Time Series Forecasting

Authors:
Paulo Cortez;Miguel Rocha;José Neves
Affiliations:
-;-;-
Venue:
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Year:
2001

Citing 3
Cited 1

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications

Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Artificial Intelligence: Structures and Strategies for Complex Problem Solving

Artificial Intelligence: Structures and Strategies for Complex Problem Solving

Evolving Time Series Forecasting ARMA Models

Journal of Heuristics

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Abstract

Nowadays, the ability to forecast the future, based only on past data, leads to strategic advantages, which may be the key to success in organizations. Time Series Forecasting allows the modeling of complex systems as black-boxes, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. On the other hand, Genetic and Evolutionary Algorithms (GEAs) are a novel technique increasingly used in Optimization and Machine Learning tasks. The present work reports on the forecast of several Time Series, by GEA based approaches, where Feature Analysis, based on statistical measures is used for dimensionality reduction. The handicap of the evolutionary approach is compared with conventional forecasting methods, being competitive.