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

Yıl 2021, Cilt: 24 Sayı: 1, 1 - 15, 01.03.2021

Anıl Utku , Muhammet Ali Akcayol

https://doi.org/10.2339/politeknik.620613

Cited By: 3

Öz

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.

Anahtar Kelimeler

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

Derin Öğrenme Tabanlı Model ile Bir Olayın Sonraki Olma Zamanının Tahmini

Öz

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

Kaynakça

• 1. Adel, H., Salheen, M., Mahmoud, R. A., Crime in relation to urban design, Case study: The Greater Cairo Region, Ain Shams Engineering Journal, 7(3), 925-938, 2016.
• 2. Yu, C. H., Ward, M. W., Morabito, M., Ding, W., Crime forecasting using data mining techniques, IEEE 11th international conference on data mining workshops, 779-786, 2011.
• 3. Wang, B., Zhang, D., Zhang, D., Brantingham, P. J., Bertozzi, A. L., Deep learning for real time crime forecasting, arXiv, 2017.
• 4. Stalidis, P., Semertzidis, T., Daras, P., Examining Deep Learning Architectures for Crime Classification and Prediction, arXiv, 2018.
• 5. Azeez, J., Aravindhar, D. J., Hybrid approach to crime prediction using deep learning, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1701-1710, 2015.
• 6. Han, J., Data mining: concepts and techniques, Morgan Kaufmann, Massachusetts, ABD, 2012.
• 7. Cesario, E., Catlett, C., Talia, D., Forecasting crimes using autoregressive models, 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 795-802, 2016.
• 8. Fu, R., Zhang, Z., Li, L., Using LSTM and GRU neural network methods for traffic flow prediction, Chinese Association of Automation (YAC), Youth Academic Annual Conference, 324-328, 2016.
• 9. Kang, H. W., Kang, H. B., Prediction of crime occurrence from multi-modal data using deep learning, PloS one, 12(4), 2017.
• 10. Zheng, J., Xu, C., Zhang, Z., Li, X., Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network, Information Sciences and Systems (CISS), 2017.
• 11. Kouziokas, G. N., The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment, Transportation research procedia, 24, 467-473, 2017.
• 12. Lin, T., Guo, T., Aberer, K., Hybrid neural networks for learning the trend in time series, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2273-2279, 2017.
• 13. McNally, S., Roche, J., Caton, S., Predicting the price of Bitcoin using Machine Learning, 2018 26th Euromicro International Conference, 339-343, 2018.
• 14. Tian, C., Ma, J., Zhang, C., Zhan, P., A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network, Energies, 2018.
• 15. Stec, A., Klabjan, D., Forecasting Crime with Deep Learning, arXiv, 2018.
• 16. United Nations Settlements Programme, The state of the world’s cities 2004/2005: Globalization and urban culture, Earthscan, 2004.
• 17. Adhikari, R., Agrawal, R. K., An introductory study on time series modeling and forecasting, arXiv, 2013.
• 18. Wang, B., Luo, X., Zhang, F., Yuan, B., Bertozzi, A. L., Brantingham, P. J., Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data, arXiv, 2018.
• 19. Chen, X., Cho, Y., Jang, S. Y., Crime prediction using Twitter sentiment and weather, 2015 Systems and Information Engineering Design Symposium, 63-68, 2015.
• 20. Munjal, K. G., Silverman, R. A., Freese, J., Braun, J. D., Kaufman, B. J., Isaacs, D., & Prezant, D. J., Utilization of emergency medical services in a large urban area: description of call types and temporal trends, Prehospital Emergency Care, 15(3), 371-380, 2011.
• 21. Martínez-Álvarez, F., Troncoso, A., Asencio-Cortés, G., Riquelme, J. C., A survey on data mining techniques applied to electricity-related time series forecasting, Energies, 8(11), 13162-13193, 2015.
• 22. Chatfield, C., The analysis of time series: An introduction, CRC press, 2016.
• 23. Granger, C. W. J., Newbold, P., Forecasting economic time series, Academic Press, 2014.
• 24. Osmanoğlu, B., Sunar, F., Wdowinski, S., Cabral-Cano, E., Time series analysis of InSAR data: Methods and trends, ISPRS Journal of Photogrammetry and Remote Sensing, 115, 90-102, 2016.
• 25. Weigend, A. S., Time series prediction: forecasting the future and understanding the past, Routledge, 2018.
• 26. Kumar, S. V., Vanajakshi, L., Short-term traffic flow prediction using seasonal ARIMA model with limited input data, European Transport Research Review, 7(3), 2015.
• 27. Montgomery, D. C., Jennings, C. L., Kulahci, M., Introduction to time series analysis and forecasting, John Wiley & Sons, 2015.
• 28. Brockwell, P. J., & Davis, R. A., Introduction to time series and forecasting. Springer, 2016.
• 29. Box, G. E., Jenkins, G. M., Reinsel, G. C., Ljung, G. M., Time series analysis: forecasting and control, John Wiley & Sons, New Jersey, ABD, 2015.
• 30. Valipour, M., Banihabib, M. E., Behbahani, S. M. R., Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir, Journal of Hydrology, 476, 433-441, 2013.
• 31. Siami-Namini, S., Namin, A. S., Forecasting Economics and Financial Time Series: ARIMA vs. LSTM, ArXiv, 2018.
• 32. Wang, W. C., Chau, K. W., Xu, D. M., Chen, X. Y., Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition, Water Resources Management, 29(8), 2655-2675, 2015.
• 33. Längkvist, M., Karlsson, L., Loutfi, A., A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognition Letters, 42, 11-24, 2014.
• 34. Gamboa, J. C. B., Deep learning for time-series analysis, arXiv, 2017.
• 35. Graupe, D., Principles of artificial neural networks, World Scientific, 2013.
• 36. Karayiannis, N., & Venetsanopoulos, A. N., Artificial neural networks: learning algorithms, performance evaluation, and applications, Springer Science & Business Media, Berlin, Almanya, 2013.
• 37. Malhotra, P., Vig, L., Shroff, G., Agarwal, P., Long short term memory networks for anomaly detection in time series, Proceedings Presses universitaires de Louvain, 2015.
• 38. Fan, J., Ma, C., Zhong, Y., A Selective Overview of Deep Learning, arXiv:1904.05526, 2019.
• 39. Grandell, J., Time Series Analysis Lecture notes. KTH Stockholm University, 2015.
• 40. Data World. Baltimore crime dataset. https://data.world/data-society/city-of-baltimore-crime-data. 2016. Erişim tarihi Mayıs 5, 2019.
• 41. Kaggle. Mongomery country emergency call dataset. https://www.kaggle.com/mchirico/montcoalert. 2018. Erişim tarihi 23/08/2019