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Real-world time-series are rarely purely linear or nonlinear and often contain both these patterns. Therefore, in this article, we have developed four hybrid models by combining linear seasonal autoregressive integrated moving average (SARIMA) and nonlinear support vector regression (NLSVR) models for time-series forecasting. Further, particle swarm optimization (PSO), which is a very efficient population-based...
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