Abstract
Forecasting analysis is a common research topic these days. The development in this area has allowed organizations to retrieve useful information and make important decisions based on the forecast results. Different forecasting models are used to model data with different characteristics as each of the forecasting model has its own strength and weakness. As such, Hybrid Prophet-LSTM that combines Long Short-Term Memory (LSTM) and FBProphet (Prophet) is introduced. This study aims to examine the effectiveness of the hybrid model and the influence of holiday effect to the forecast result. Weighted Mean Absolute Percentage Error (WMAPE), Mean Absolute Deviation (MAD), \({R}^{2}\) value, and Root mean square error (RMSE) were used to evaluate the performance of the proposed hybrid model. The proposed Hybrid Prophet-LSTM is found to outperform both the standalone LSTM and Prophet, and holiday effect shows high attitude of influence to the forecast result.
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Kong, Y.H., Lim, K.Y., Chin, W.Y. (2021). Time Series Forecasting Using a Hybrid Prophet and Long Short-Term Memory Model. In: Mohamed, A., Yap, B.W., Zain, J.M., Berry, M.W. (eds) Soft Computing in Data Science. SCDS 2021. Communications in Computer and Information Science, vol 1489. Springer, Singapore. https://doi.org/10.1007/978-981-16-7334-4_14
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