Designing Morphological/Rank/Linear Filters via Modified Genetic Algorithm for Time Series Forecasting

• Ricardo de A. Araújo UFPE
• Tiago A. E. Ferreira UFPE

Resumo

This paper presents an evolutionary approach for designing Morphological/Rank/Linear (MRL) filters for time series forecasting. It consists of an evolutionary model composed of a MRL filter and a modified Genetic Algorithm (GA) with optimal genetic operators to accelerate the search convergence. The proposed method performs an evolutionary search for the minimum number of time lags (and their corresponding specific positions) to represent the time series, as well as the parameters of the MRL filter, defined by mixing parameter (λ), rank (r), linear Finite Impulse Response (FIR) filter (b) and Morphological/Rank (MR) filter (a) coefficients. An experimental analysis is conducted with the proposed method using two real world time series and five well-known performance measurements, demonstrating good performance of MRL filtering systems for time series forecasting.

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