Derin Öğrenme Tabanlı Model ile Bir Olayın Sonraki Olma Zamanının Tahmini

Bazı olayların olmadan önce olma zamanının tahmin edilebilmesine yönelik çalışmalar uzun yıllardır devam etmektedir. Böylelikle olay ortaya çıktığında meydana gelecek zararı minimuma indirmek veya elde edilecek faydayı maksimum yapmak amaçlanır. Bir müşterinin sonraki davranışının tahmini, doğal afetlerin sonraki olma zamanının tahmini, belirli bir zaman aralığında gelecek talep sayısının tahmini gibi çok farklı alanlarda sonraki olayların tahminine yönelik çalışmalar giderek artmaktadır. Ancak, literatürde suçların ve acil çağrıların meydana gelmeden önce sonraki olma zamanının ve olay türünün tahminine yönelik başarılı sonuç veren bir çalışma bulunmamaktadır. Suç analizi, tehdit altındaki bölgelerin güvenliğini sağlamayı, suç işlenme oranını azaltmayı ve kolluk kuvveti gücünden tasarruf etmeyi amaçlayan bir araştırma alanıdır. Suçun işlendikten kısa süre sonra çözümlenmesine yönelik çalışmalarda güncel teknolojilerin kullanımıyla yüksek başarı elde edilmektedir. Benzer şekilde acil çağrı analizi ile çağrılara yanıt süresi kısaltılmakta ve kaynak kullanımı optimize edilmektedir. Bu çalışmada, suç ve acil çağrı analizine yönelik derin öğrenme tabanlı bir tahmin modeli geliştirilmiştir. Geliştirilen model ile suç işlenmeden önce ve acil çağrı gelmeden önce bir sonraki olma zamanı tahmin edilmektedir. Geliştirilen model ile elde edilen sonuçlar, istatistiksel zaman serisi tahminleme yöntemlerinden olan ARIMA ile kapsamlı bir şekilde karşılaştırılmıştır. Deneysel sonuçlar, geliştirilen derin öğrenme tabanlı modelin ileriye dönük zaman tahmininde ARIMA’dan daha başarılı olduğunu göstermiştir.

Anahtar Kelimeler:

Suç Analizi, Acil Çağrı Analizi, Zaman Serisi Analizi, Derin Öğrenme, ARIMA

Prediction of the Next Time of an Event with Deep Learning Based Model

Studies have been going on for many years to predict the time before some events happen. Thus, it is aimed to minimize the damage that occurs when the event occurs or to maximize the benefit to be obtained. Studies on the prediction of subsequent events in many different areas, such as the prediction of the subsequent behavior of a customer, the prediction of the subsequent occurrence of natural disasters, the estimate of the number of future demands in a given time interval, are gradually increasing. However, in the literature, there is no successful study for predicting the time and type of event before the occurrence of crimes and emergency calls. Crime analysis is a field of research aimed at securing the threatened areas, reducing the rate of crime and saving law enforcement. High success is achieved with the use of up-to-date technologies in the efforts to resolve the crime shortly after it is committed. Similarly, emergency call analysis reduces response time and optimizes resource usage. In this study, a deep learning based prediction model for crime and emergency call analysis has been developed. With the developed model, the time of the next crime and the time of the next emergency call are predicted. The results obtained with the developed model has been compared with ARIMA which is one of the statistical time series prediction methods. Experimental results have shown that the developed deep learning-based model is more successful than ARIMA in forward-looking event time prediction.

Keywords:

Crime analysis, emergency call analysis, time series analysis, deep learning, ARIMA,

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