Akademik Veri Yönetim Sistemi
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.37, sa.2, ss.1019-1032, 2022 (SCI-Expanded)
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.