Probabilistic-Based Forecasting Method For Time Series Datasets

Year 2023, , 563 - 573, 30.04.2023

Abdullatif Baba

https://doi.org/10.29130/dubited.1022265

Abstract

In this paper, a new probabilistic technique (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 (Istanbul Stock Exchange Index), TAIEX (Taiwan Stock Exchange), and ABC (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

Forecasting; Time series dataset, MMPF, Evaluation Metrics

Zaman Serisi Veri Kümeleri İçin Olasılığa Dayalı Tahmin Yöntemi

Abstract

Bu makalede, zaman serisi veri kümelerini tahmin etmek için Çoklu Model Parçacık Filtresinin (ÇMPF) bir çeşidi olarak düşünülebilecek yeni bir olasılık tabanlı teknik kullanılmaktadır. Yöntemimizin güvenilirlik performansı art arda altmış günden oluşan sanal bir rastgele senaryo kullanılarak kanıtlanmıştır. İkinci, üçüncü ve dördüncü günde tahmin edilen durumlar ile gerçekte karşılık gelen değerler arasında büyük bir fark ortaya çıkmaktadır; yöntemimiz kullanılarak tahmin edilen durumlar gerçek değerlere doğru hızla yakınsarken, tek başına klasik bir lineer model kullanıldığında büyük miktarda sapma göstermektedir. Makalede yaklaşımımızın performansı; Kök-Ortalama-Kare Hatası, Ortalama Mutlak Yüzde Hatası ve Korelasyon Katsayısı performans değerlendirme ölçütleri dikkate alınarak; BIST (Borsa İstanbul Endeksi), TAIEX (Tayvan Borsa Endeksi), ve ABC (Avustralya Bira Tüketimi) zaman serisi veri kümelerine halihazırda uygulanmış olan diğer bazı tekniklerle karşılaştırılmaktadır.

Keywords

Tahmin; Zaman Serisi Veri Kümesi; ÇMPF; Değerlendirme Metrikleri

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