Hybrid Approach Wavelet Seasonal Autoregressive Integrated Moving Average Model (WSARIMA) for Modeling Time Series

Many prognosis studies have been conducted for a long time. There are many established and widely accepted prediction methods, such as linear extrapolation and SARIMA. However, their performance is far from perfect, especially when the time series is highly volatile. In this paper, we propose a hybrid prediction scheme that combines the classical SARIMA method and the wavelet transform (WT). Wavelet transform (WT) has emerged as an effective tool in decomposing time series into different components, which allows for improved prediction accuracy. However, this issue has so far been insufficiently tested and tried to predict different time series. Our goal is therefore to integrate modeling approaches as a decision support tool. The results of an empirical study show that this method can achieve high accuracy in prediction. Based on the results of the created model, it can be stated that the hybrid WSARIMA model overperformed the SARIMA model.