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Zaman Serileri Tahmininde ARIMA-MLP Melez Modeli

Authors: Oğuz Kaynar; Serkan Taştan;

Zaman Serileri Tahmininde ARIMA-MLP Melez Modeli

Abstract

Bu çalışmada zaman serilerinin tahmini için otoregresif hareketli ortalamalar(autoregressive integrated moving average-ARIMA) modeli ve çok katmanlı yapay sinir ağları (multi layer perceptron-MLP) modeli birleştirilerek bir melez model oluşturulmuştur. Melez modelde, zaman serisinin doğrusal bileşeni ARIMA modeli ile doğrusal olmayan bileşeni ise MLP modeli ile tahmin edilmiştir. ARIMA ve MLP modellerinin tek başına kullanılması ile elde edilen tahmin sonuçları Melez modelin tahmin sonuçları ile karşılaştırılarak Melez modelin tahmin performansı ölçülmüştür.

In this study, a hybrid model was created by combining autoregressive integrated moving average(ARIMA) model and multi layer perceptron(MLP) model for time series forecasting. In hybrid model lineer component of time series is forecasted by ARIMA and nonlinear component is forecasted by MLP respectively. Forecasting performance of hybrid model is measured through the forecast results obtained from the model that used only ARIMA and MLP is compared with the forecast results of hybrid model.

Country
Turkey
Related Organizations
Keywords

İşletme, Yapay Sinir Ağları;MLP;ARIMA;Melez Model, İktisat

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green