Zaman Serisi Problemlerinde Parametrik Model Seçimi İçin Bir Çerçeve

Abstract

İnsanlar yaşamlarını kolaylaştırmak için geleceğe yönelik planlamalar yapmaktadır. Bu planlamalar gelecekte karşılaşabilecek problemlere hazırlıklı olmak için önemlidir. Geleceğe yönelik hazırlıklar yapılabilmesi için de tahmin yöntemleri ön plana çıkmaktadır. Geleceğe yönelik tahminler için kullanılan verilerden birisi de zaman serileridir. Bu çalışmada zaman serisi tahminlerinde kullanılacak klasik yapay sinir ağları için en iyi modeli seçen bir çerçeve geliştirilmiştir. Klasik yapay sinir ağları olarak LSTM, CNN ve DNN modelleri kullanılmaktadır. Framework en iyi modeli seçmek için veri seti, model derinliği, kayıp fonksiyonları, model performansında minimum başarı oranı, tekrar sayısı ve optimizasyon algoritmalarını parametre olarak kullanmaktadır. Kullanıcılar bu parametreleri kendi problemlerine uygun güncelleyebilmektedir. Model seçimi içinse varsayılan olarak 7 farklı kayıp fonksiyonu ve 5 farklı optimizasyon algoritması kullanmaktadır. Model performansları Mean Avarage Error kayıp fonksiyonuyla belirlenmektedir. Framework deneylerinde, veri seti olarak Brent Ham Petrol fiyatları kullanılmış olup veri seti 9000 günlük fiyat bilgisi içermektedir. Veri seti %80 eğitim ve %20 test olarak iki bölünmüştür. Çerçeve testindeyse eğitimler 50 tekrar ile gerçekleştirilmiştir. Deneyde 7 günlük ardışık fiyat bilgisiyle 8. gündeki fiyat tahmin ettirilmiştir. Sonuç olarak 1.1239657 Mean Average Error skoru elde edilmiştir. En başarılı model, 2 katmanlı Adadelta optimizasyon algoritmasını ve Mean Square Error kayıp fonksiyonu kullanan LSTM modeli olmuştur.

Keywords

• Zaman serileri
• lstm
• cnn
• dnn
• tahminleme

A Framework for Parametric Model Selection in Time Series Problems

Abstract

People make future plans with the aim of simplifying their lives, and these plans are essential for preparing for forthcoming challenges. Forecasting methodologies take precedence in order to anticipate and plan for future events. Time series data stands out as a pivotal information type employed for predicting the future. This research introduces a framework for selecting the optimal model among classical artificial neural networks in time series forecasting. The classical artificial neural networks considered encompass the LSTM, CNN, and DNN models. The framework employs various parameters – including the dataset, model depth, loss functions, minimal success rate in model performance, epochs, and optimization algorithms – to determine the best-fitting model. Users have the flexibility to adjust these parameters to address specific issues. By default, the framework incorporates seven distinct loss functions and five optimization algorithms to facilitate model selection. The mean average error loss function is used as the metric for evaluating model performance. To validate the framework, Brent oil prices were utilized as the dataset in a series of tests, encompassing a total of 9000 daily price data points. The dataset was partitioned into 80\% for training and 20\% for testing purposes. The training iterations within the framework were 50 epochs. In the test scenarios, the price for the eighth day was predicted using price data from the preceding seven days. Consequently, a mean average error score of 1.1239657 was achieved. The results showed that the LSTM model, comprising two layers, the Adadelta optimization algorithm, and the mean square error loss function, emerged as the most successful configuration.

Keywords

• Time series
• lstm
• cnn
• dnn
• forecasting

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