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Forecasting Through Motifs Discovered by Genetic Algorithms

ABSTRACT Forecasting is very important in the present world and has vast applications in various domains like weather forecasting, stock market trading, energy forecasting, etc. One of the ways of doing forecasts is through motifs. The present paper proposes an algorithm which first discovers multi-dimensional motifs in a real-valued time series using genetic algorithm with specially designed operators. The discovered motifs are then used to predict future values of the time series. The forecast values are compared with forecast values obtained using Vector Auto-regressive (VAR) prediction model and a naïve model. The results obtained show the effectiveness of the proposed algorithm.