Probabilistic-Based Forecasting Method For Time Series Datasets

Authors : Abdullatif BABA

Pages : 563-573

Doi:10.29130/dubited.1022265

View : 12 | Download : 9

Publication Date : 2023-04-30

Article Type : Research Paper

Abstract :In this paper, a new probabilistic technique insert ignore into journalissuearticles values(a variant of Multiple Model Particle Filter-MMPF); will be used to predict time-series datasets. At first, the reliable performance of our method is proved using a virtual random scenario containing sixty successive days; a large difference between the predicted states and the real corresponding values arises on the second, third, and fourth day. The predicted states that are determined by using our method converge rapidly towards the real values while a classical linear model exhibits a large amount of divergence if used alone here. Then, the performance of our approach is compared with some other techniques that were already applied to the same time-series datasets: IEX insert ignore into journalissuearticles values(Istanbul Stock Exchange Index);, TAIEX insert ignore into journalissuearticles values(Taiwan Stock Exchange);, and ABC insert ignore into journalissuearticles values(The Australian Beer Consumption);. The performance evaluation metrics that are utilized here are the correlation coefficient, the mean absolute percentage error, and the root mean squared error.
Keywords : Tahmin, Zaman Serisi Veri Kümesi, ÇMPF, Değerlendirme Metrikleri