چکیده مقاله
Hybrid models is one of the most well known solution introduced for obtaining more accurate and more reliable results in real world modelling and forecasting problems One of the main purpose of these methods is to eliminate the drawbacks of single models in modelling various latent patterns in data Thus in this study, in order to improve forecasting accuracy of Auto regression Integrated Moving Average ARIMA and multilayer perceptron MLP models and reduce the complexity of time series forecasting calculations, a novel hybrid model based on the principle of divide and conquer is represented In the proposed method, in the first modelling phase, the time series, which is potentially has complexity, high volatility, high frequency and consists of several patterns, is decomposed to its Intrinsic Mode Functions IMFs by employing Ensemble Empirical Mode Decomposition EEMD In the next step, each of these simplified decomposed components is forecasted using the ARIMA and MLP models Finally, the forecasted value of each of the IMFs are combined to obtain final hybrid result The numerical forecasting results for a benchmark dataset, that is, the wind power indicated that the proposed methods especially EEMD MLP method can improve the performance of the ARIMA and MLP model in forecasting two volatile bench mark data sets Thus, the proposed hybrid model can introduce as an effective predicting tool for high variation time series
کلیدواژهها
نویسندگان
Pegah Amini
Department of Industrial and systems Engineering, Isfahan University of Technology, Isfahan, Iran
Mehdi Khashei
Department of Industrial and systems Engineering, Isfahan University of Technology, Isfahan, Iran
شیوه ارجاع
Amini, Pegah and Khashei, Mehdi,1399,A Hybrid Empirical Mode Decomposition based ARIMA and MLP Models for Wind Power Time Series Forecasting,6th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
مجموعه مقالات ششمین کنفرانس بین المللی مهندسی صنایع و سیستمها (ICISE ۲۰۲۰)19 شهریور 1399 · مشهد