@article{article_1649017, title={Time Series Forecasting Transformer and Prophet Model}, journal={İstatistik Araştırma Dergisi}, volume={15}, pages={9–16}, year={2025}, author={Yılmaz, Sibel and Yalaz, Seçil and Çalik, Sinan}, keywords={Time series forecasting, Machine learning, Deep learning, Prophet model, Transformer model}, abstract={A time series is a sequence of temporal data obtained at specific intervals. These intervals can represent equal time periods such as daily, weekly, monthly, or yearly, or irregular and unequal time periods. Time series forecasting involves predicting future values of the target variable using its past values. This process involves identifying temporal variations such as trend, seasonality, and noise to make predictions about future values. In time series analysis, machine learning include data analysis, preprocessing, normalization, transformations, time based features, error optimization, and model accuracy optimization. Machine learning methods provide greater efficiency compared to traditional methods, especially when the volume and complexity of data and variables increase. Deep learning, in particular, offers advantages in time series forecasting by capturing long-term dependencies and handling large and complex datasets. Machine learning and deep learning models can achieve high accuracy, interpretability, and fast results for multivariate time series forecasting. In this study, the Prophet model and the Transformer model, which is one of the most advanced methods of recent times and has shown effective results in many fields, were used for future time series forecasting. The study focused on the inclusion of numerous endogenous and exogenous factors, the comparison of the performance of these two methods, the variable augmentation and the calculation of variable importances by incorporating error weights for each variable into the models, and the combined use of these methods to achieve high accuracy.}, number={1}, publisher={TÜİK}