Zaman serileri analizi için optimize ARIMA-YSA melez modeli

Yıl 2022, Cilt: 37 Sayı: 2, 1019 - 1032, 28.02.2022

Mahmut Burak Erturan , Fahriye Merdivenci

https://doi.org/10.17341/gazimmfd.889513

Cited By: 3

Öz

Zaman serileri analizi alanında son yıllarda birden çok modelin bir arada kullanıldığı melez modeller ortaya konulmaktadır. Literatürde yer alan en önemli melez model sınıflarından biri ARIMA-Yapay Sinir Ağları (YSA) melez model sınıfıdır. Gerçek hayatta karşılaşılan zaman serilerinin genellikle doğrusal ve doğrusal olmayan özellikleri bir arada taşıması, ARIMA-YSA melez modellerin tahmin performanslarının yüksek olmasını sağlamaktadır. Bu çalışmada optimizasyon tabanlı özgün bir ARIMA-YSA melez model ortaya konulmaktadır. İleri sürülen melez model, zaman serisini doğrusal ve doğrusal olmayan iki serinin toplamı olarak kabul etmektedir. İki aşamalı modelin ilk aşamasında, doğrusal ve doğrusal olmayan bileşenlerin elde edilmesi amacıyla ARIMA ve YSA modelleri ile gerçek seri bir en küçük kareler optimizasyonu sürecinden geçmektedir. İkinci aşamada ise doğrusal bileşenin hataları doğrusal olmayan bileşene aktarılarak doğrusal olmayan bileşen revize edilmekte ve YSA ile tekrar modellenmektedir. Tahmin performansının tespiti için Optimize ARIMA-YSA (OptAA) melez modeli, ARIMA, YSA, literatürdeki başlıca melez ARIMA-YSA modelleri ve rassal yürüyüş modeli ile karşılaştırılmaktadır. Uygulama aşaması için literatürde sıklıkla kullanılan üç farklı zaman serisi seçilmiştir. Elde edilen sonuçlar OptAA melez modelin özellikle görece kısa dönem tahmin performansının diğer modellere göre oldukça yüksek olduğunu ve zaman serisi analizi alanında oldukça güçlü bir yöntem olduğunu göstermektedir.

Anahtar Kelimeler

Zaman Serisi Analizi, ARIMA, Yapay Sinir Ağları, Optimize ARIMA-YSA Melez Model, En Küçük Kareler Optimizasyonu

Optimized ARIMA-ANN hybrid model for time series analysis

Öz

In recent years, hybrid models, using more than one models together, are presented in the field of time series analysis. One of the most important hybrid model classes is ARIMA-Artificial Neural Networks (ANN) hybrids. Time series encountered in real life usually carry linear and nonlinear characteristics together, which causes high forecasting performance of ARIMA-ANN hybrid models. In this study, a novel optimization based ARIMA-ANN hybrid model is presented. Proposed hybrid model assumes time series is the sum of linear and nonlinear two series. In the first stage of the two staged model, ARIMA and ANN models with real time series pass through a least squares optimization process to obtain linear and nonlinear components. In the second stage, error values of the linear component are transferred to nonlinear component, nonlinear component is revised and remodeled with ANN. Optimized ARIMA-ANN (OptAA) hybrid model is compared with ARIMA, ANN, main ARIMA-ANN hybrid models in literature and random walk model to determine the forecasting performance. Three different time series used often in the literature are chosen for the application purposes. Obtained results show that OptAA hybrid model has higher performance than other models especially in relatively short term forecasting and is a very powerful methodology in time series analysis field.

Anahtar Kelimeler

Time Series Analysis, ARIMA, Artificial Neural Networks, Optimized ARIMA-ANN Hybrid Model, Least Squares Optimization

Kaynakça

• Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., Time Series Analysis: Forecasting and Control, John Wiley & Sons, New Jersey, A.B.D., 2015.
• Zhang, G.P., Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model, Neurocomputing, 50, 159-175, 2003.
• Santos Júnior, D.S. de O., de Oliveira J.F.L., de Mattos Neto, P.S.G., An intelligent hybridization of ARIMA with machine learning models for time series forecasting, Knowl.-Based Syst., 175, 72-86, 2019.
• Somers, M.J. ve Casal, J.C., Using Artificial Neural Networks to Model Nonlinearity The Case of the Job Satisfaction–Job Performance Relationship, Organ. Res. Methods, 12(3), 403-417, 2009.
• Yan, H. ve Zou, Z., Application of a Hybrid ARIMA and Neural Network Model to Water Quality Time Series Forecasting, J. Converg. Inf. Technol., 8(4), 59-70, 2013.
• Hajirahimi, Z. ve Khashei, M., Hybrid structures in time series modeling and forecasting: A review, Eng. Appl. Artif. Intell., 86, 83-106, 2019.
• E, J., Ye, J., Jin, H., A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting, Physica A, 527, 121454, 2019.
• Zhang, J., Tan, Z., Wei, Y., An adaptive hybrid model for day-ahead photovoltaic output power prediction, J. Cleaner Prod., 244, 118858, 2020.
• Lotfi, K., Bonakdari, H., Ebtehaj, I., Mjalli, F.S., Zeynoddin, M., Delatolla, R., Gharabaghi, B., Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology, J. Environ. Manage., 240, 463-474, 2019.
• Kaytez, F., A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption, Energy, 197, 117200, 2020.
• Xu, S., Chan, H.K., Zhang, T., Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach, Transp. Res. Part E Logist. Transp. Rev., 122,169-180, 2019.
• Saxena, H., Aponte, O., McConky, K.T., A hybrid machine learning model for forecasting a billing period’s peak electric load days, Int. J. Forecasting, 35, 1288-1303, 2019.
• Khan, M.M.H., Muhammad, N.S., El-Shafie, A., Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting, J. Hydrol., 590, 125380, 2020.
• Safari, A. ve Davallou, M., Oil price forecasting using a hybrid model, Energy, 148, 49-58, 2018.
• Mahalakshmi, N., Umarani, P.R., Selvaraj, R.S., Forecasting the Tamil Nadu Rainfall Using Hybrid ARIMA–ANN Model, Int. J. Recent Sci. Res., 5(3), 566-569, 2014.
• Suhermi, N., Suhartono, Prastyo, D.D., Ali, B., Roll motion prediction using a hybrid deep learning and ARIMA model, Procedia Comput. Sci., 144, 251-258, 2018.
• Wang, X. ve Meng, M., A Hybrid Neural Network and ARIMA Model for Energy Consumption Forecasting, J. Comput., 7(5), 1184-1190, 2012.
• Hwang, K.P. ve Day, Y.J., Tourism Revenue Forecasting: A Hybrid Model Approach, Actual Probl. Econ., 141(3), 473-483, 2013.
• Babu, C.N. ve Reddy, B.E., A Moving-Average Filter Based Hybrid ARIMA–ANN Model for Forecasting Time Series Data, Appl. Soft Comput., 23, 27-38, 2014.
• Aburto, L., ve Weber, R., Improved Supply Chain Management Based on Hybrid Demand Forecasts, Appl. Soft Comput., 7(1), 136-144, 2007.
• Wang, L., Zou, H., Su, J., Li, L., Chaudrhy, S., An ARIMA-ANN Hybrid Model for Time Series Forecasting, Syst. Res. Behav. Sci., 30, 244-259, 2013.
• Khashei, M. ve Bijari, M., A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting, Appl. Soft Comput., 11, 2664-2675, 2011.
• Büyükşahin¸ Ü.Ç. ve Ertekin, Ş., Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition, Neurocomputing, 361, 151-163, 2019.
• Hyndman, R.J. ve Khandakar, Y., Automatic Time Series Forecasting: The Forecast Package for R, J Stat. Softw., 27(3), 1-22, 2008.
• Zhang, G.P., Patuwo, B.E., Hu, M.Y., A simulation study of artificial neural networks for nonlinear time-series forecasting, Comput. Oper. Res., 28, 381-396, 2001.
• Kaastra, I. ve Boyd, M., Designing a Neural Network For Forecasting Financial and Economic Time Series, Neurocomputing, 10, 215-236. 1996.
• Chakra, C.N.C., Song, K.Y., Saraf, D.N., Gupta, M.M., Production Forecasting of Petroleum Reservoir Applying Higher-Order Neural Networks (HONN) With Limited Reservoir Data, Int. J. Comput. Appl., 72(2), 23-35, 2013.
• Panchal, G., Ganatra, A., Kosta, Y.P., Panchal, D., Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers, Int. J. Comput. Theory Eng., 3(2), 332-337, 2011.
• Makridakis, S., Spiliotis, E., Assimakopoulos, V., The M4 Competition: 100,000 time series and 61 forecasting methods, Int. J. Forecasting, 36, 54–74, 2020.