Kapılı Tekrarlayan Hücreler Tabanlı Bulanık Zaman Serileri Tahminleme Modeli

Year 2023, , 677 - 692, 28.06.2023

Serdar Arslan

https://doi.org/10.35414/akufemubid.1175297

Cited By: 1

Abstract

Zaman serisi tahminleme hava durumu, iş dünyası, satış verileri ve enerji tüketimi tahminleme gibi bir çok alanda uygulama alanına sahiptir. Bu alanlarda tahminleme yaparken kesin sonuçlar elde etmek çok önemlidir ama aynı zamanda zaman serilerinin karmaşık ve de belirsizlik içeren veriler olması nedeniyle çok zordur. Günümüzde, derin öğrenme metotları bu alanda klasik metotlara göre daha iyi sonuçlar vermektedir. Fakat literatürde bulanık zaman serileri tahminleme konusunda çok az çalışma vardır. Bu çalışmada, zaman serilerindeki karmaşıklığın ve belirsizliğin doğurduğu problemleri yok etmek için Yinelemeli sinir Ağları ile bulanık time serilerini bir arada kullanan bir model ortaya konmuştur. Bu çalışmada, Kapılı Tekrarlayan Hücreler kullanarak geçmiş veriler ile bulanık verilerin üyelik değerleri birleştirilerek tahminleme değeri hesaplanmıştır. Ayrıca, bu çalışmadaki model ilk seviye bulanık ilişkileri ele alabildiği gibi, çoklu seviye bulanık ilişkileri de kapsamaktadır. Testlerde literatürde var olan çalışmalar ilgili model ile iki açık veri seti ile karşılaştırılmış olup bahsi geçen modelin daha iyi veya benzer sonuçlar verdiği gözlemlenmiştir. Ayrıca model Covid-19 verileri kullanılarak da test edilmiş ve Uzun-Kısa Süreli Bellek modellerinden daha iyi sonuç vermiştir.

Keywords

Kapılı Tekrarlayan Hücreler, Zaman Serisi Tahminleme, Bulanık Zaman Serisi, Derin Öğrenme

Gated recurrent unit network-based fuzzy time series forecasting model

Abstract

Time series forecasting has lots of applications in various industries such as weather, business, retail and energy consumption forecasting. Accurate prediction in these applications is very important and also difficult task because of complexity and uncertainty of time series. Nowadays, using deep learning methods is a popular approach in time series forecasting and shows better performance than classical methods. However, in the literature, there are few studies which use deep learning methods in fuzzy time series (FTS) forecasting. In this study, we propose a novel FTS forecasting model which is based upon hybridization of Recurrent Neural Networks with FTS to deal with complexity and also uncertanity of these series. The proposed model utilizes Gated Recurrent Unit (GRU) to make prediction by using combination of membership values and also past value from original time series data as model input and produce real forecast value. Moreover, the proposed model can handle first order fuzzy relations as well as high order ones. In experiments, we have compared our model results with those of state-of-art methods by using two real world datasets; The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Nikkei Stock Average. The results indicate that our model outperforms or performs similar to other methods. The proposed model is also validated by using Covid-19 active case dataset and shows better performance than Long Short-term Memory (LSTM) networks.

Keywords

Gated Recurrent Unit, Time Series Forecasting, Fuzzy Time Series, Deep Learning

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