Zaman serileri analizi için optimize ARIMA-YSA melez modeli

Abstract

A novel ARIMA-ANN hybrid model for time series analysis based on least squares optimization  Performance comparison with seven other models by applying to three well known time series data  High forecasting performance, especially for relatively shorter term forecasting horizons , Figure A. Graphic Illustration of Optimized ARIMA-ANN Hybrid Model Purpose: The purpose of this study is to present a novel ARIMA-ANN hybrid model for time series analysis. Proposed optimized ARIMA-ANN (OptAA) hybrid model is applied to three well-known time series data with varying forecasting horizons. For determination of the forecasting performance, results are compared with the results of other models. Theory and Methods: Proposed model uses a least squares optimization of ARIMA and ANN models of the time series data to decompose it into linear and nonlinear components. After the first decomposition, error series of the linear part is transferred to the nonlinear component to revise the nonlinear part, which is then remodeled with ANN. The sum of the ARIMA model forecast of the linear part and ANN forecast of the revised nonlinear part is the final forecast of the hybrid model. Three time series data, Wolf's sunspot, Canadian lynx and GBP/USD exchange rate are used for forecasting performance comparison purposes. Proposed hybrid model's forecasting performance is compared with four major ARIMA-ANN hybrid models, ARIMA, ANN and random walk model. Results:Obtained results show that OptAA hybrid model is a very powerful methodology for time series forecasting.Especially for short term forecasting horizons proposed hybrid model shows better performance than other compared models. Conclusion:OptAA hybrid model is open for further research. Testing the model with different neural network parameters such that learning algorithm, network architecture, activation functions etc. is possible. Also applying the model to different time series and forecasting horizons helps to improve the generalization of the model.

Türkiye’de Sığır Eti Üretiminin ARIMA (Box-Jenkins) Modeli ile Öngörüsü

“…göre değişimini içeren verilere denilmektedir (Tatoğlu, 2018). Oluşturulan veri setinin söz konusu dönemdeki izlediği gelişimi görme açısından gerekli olan zaman serilerinin çözümlenmesi için (Çamoğlu ve Akıncı, 2012), değişkenlerin zaman içerisinde aldıkları değerlerin bulunan örüntülerini kullanarak gelecek yıllarda alabileceği değerlerin tahmin edilmesi amacıyla zaman serisi analizi yapılmaktadır (Erturan, 2017).…”