A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands

The prediction of time series is an important task both in academic research and in industrial applications. Firstly, an appropriate prediction method has to be chosen. Subsequently, the parameters of this prediction method have to be adjusted to the time series evolution. In particular, an accurate prediction of future customer demands is often difficult, due to several static and dynamic influences. As a promising prediction method, we propose a lazy learning algorithm based on phase space reconstruction and k-nearest neighbor search. This algorithm originates from chaos theory and nonlinear dynamics. In contrast to widely used linear prediction methods like the Box-Jenkins ARIMA method or exponential smoothing, this method is appropriate to reconstruct additional influences on the time series data and consider these influences within the prediction. However, in order to adjust the parameters of the prediction method to the observed time series evolution, a reasonable optimization algorithm is required. In this paper, we present a genetic algorithm for parameter optimization. In this way, the prediction method is automatically fitted accurately and quickly to observed time series data, in order to predict future values. The performance of the genetic algorithm is evaluated by an application to different time series of customer demands in production networks. The results show that the genetic algorithm is appropriate to find suitable parameter configurations. In addition, the prediction results indicate an improved forecasting accuracy of the proposed prediction algorithm compared to linear standard methods.