Comparison of NAIVE, SARIMA, and SARIMAX Models in Short-Term and Long-Term Forecasting of Google Search Trends
DOI:
https://doi.org/10.26438/ijsrcse.v13i2.665Keywords:
Google Search Trends, Forecasting Methods, Naïve Method, SARIMA, SARIMAX, Time Series AnalysisAbstract
This study examines forecasting methods—Naïve, SARIMA, and SARIMAX—to enhance the accuracy of Google search trend predictions in the fitness industry, specifically for the keyword "Gym." The Naïve method serves as a baseline comparison, SARIMA incorporates seasonal, autoregressive, and moving average components for improved trend detection, while SARIMAX extends SARIMA by integrating exogenous variables. Historical Google search data from 2005 to 2015 is used for model evaluation, with performance assessed using MAE, RMSE, and MAPE metrics. The findings indicate that SARIMAX, which accounts for external influences, provides the highest accuracy. Additionally, short-term models exhibit greater responsiveness to seasonal variations. This study offers insights into the strengths and limitations of each method, assisting practitioners in selecting the most suitable approach for improving search trend forecasting and supporting data-driven decision-making in the fitness industry.
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