Forecasting Nonlinear Time Series with ARIMA, ANN, and Hybrid Models: A Case Study on Inflation Rate in Sri Lanka
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Abstract
In time series forecasting, hybrid models combining autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) have gained prominence due to their ability to capture both linear and nonlinear patterns within data. ARIMA models are effective at modeling linear relationships, while ANNs are adept at handling complex nonlinear structures. However, each model has its limitations when used independently. This study presents a hybrid model that integrates the strengths of both ARIMA and ANN to forecast the monthly inflation rate in Sri Lanka using historical data from 1988 to 2018. Our findings demonstrate that the proposed hybrid model outperforms the standalone ARIMA and ANN models, particularly in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). By leveraging the complementary strengths of ARIMA and ANN, this hybrid approach provides a robust forecasting framework for handling the diverse structural complexities of time series data
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