Design of Neural Networks for Time Series Prediction Using Case-Initialized Genetic Algorithms
Teresa B. Ludermir · 2008
One of the major objectives of time series analysis is the design of time series models, used to support the decision-making in several application domains. Among the existing time series models, we highlight the Artificial Neural Networks (ANNs), which offer greater computational power than the classical linear models. However, as a drawback, the performance of ANNs is more vulnerable to wrong design decisions. One of the main difficulties of ANN’s design is the selection of an adequate network’s architecture. In this work, we propose the use of Case-Initialized Genetic Algorithms to help in the ANN’s design. We maintain a case base in which each case associates a time series to a wellsucceeded neural network used to predict it. Given a new time series, the most similar cases are retrieved and their solutions are inserted in the initial population of the Genetic Algorithms (GAs). Next, the GAs are executed and the best generated neural model is returned. In the undergone tests, the Case-Initialized GAs presented a better generalization performance than the GAs with random initialization. We expect that the results will be improved as more cases are inserted