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Hiyerarşik Zamansal Bellek Modeli ile Meteorolojik Verilerdeki Anomalilerin Tespiti: Kazakistan Örneği Üzerine Bir Çalışma

Yıl 2024, Cilt: 36 Sayı: 1, 481 - 498, 28.03.2024
https://doi.org/10.35234/fumbd.1425635

Öz

Atmosferik olayları inceleyen meteorolojide, sıcaklık, yağış ve rüzgar hızı gibi çeşitli özellikleri temsil eden veriler belirli bir süre boyunca düzenli olarak toplanmaktadır. Verilerdeki beklenmedik eğilimler anormal bir durumun yaklaşmakta olduğunu gösterebilmektedir. Bu nedenle, zaman serisi verileri potansiyel meteorolojik risklerin erken tespitinde önemli bir rol oynamaktadır. Ancak doğru ve güvenilir analizlerin gerçekleştirilmesinde ve anomali tespitinde karmaşık birçok parametreyi göz önünde bulundurarak etkin modelleri uygulamak önemli bir kriterdir. Bu çalışmada, dünyanın en büyük dokuzuncu yüzölçümüne sahip Kazakistan için 1 Ocak 2019 ile 30 Haziran 2023 tarihleri arasında toplanan farklı özelliklerdeki meteorolojik verileri içeren bir veri seti kullanılarak makine öğrenmesi tabanlı anomali tespiti gerçekleştirilmiştir. Anomali tespiti için uzun vadeli bağımlılıkları modelleyerek daha doğru tahminler sağlayabilen ve zaman serisi problemlerinin çözümünde etkin sonuçlar üreten Hiyerarşik Zamansal Bellek (HTM) modeli kullanılmıştır. Tespit edilen anomaliler eşik değerlerine bağlı olarak çeşitli seviyelerde raporlanmıştır. Ayrıca, anomali tespitlerini daha hassas bir şekilde analiz etmek için, değişkenler arasındaki monotonik ilişkinin gücünü ve yönünü belirlememizi sağlayan Spearman modeli kullanılarak korelasyonlar hesaplanmıştır. Çalışmanın bulguları, HTM modelinin meteorolojik özelliklere ilişkin zaman serisi verilerinin kullanıldığı AD problemlerinde etkin bir araç olduğunu göstermektedir.

Kaynakça

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Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan

Yıl 2024, Cilt: 36 Sayı: 1, 481 - 498, 28.03.2024
https://doi.org/10.35234/fumbd.1425635

Öz

In meteorology, which studies atmospheric events, data representing various properties such as temperature, rainfall, and wind speed are collected regularly over a certain period. Unexpected trends in the data may indicate that an abnormal situation is approaching. Therefore, time series (TS) data play an essential role in the early detection of potential meteorological risks. However, applying effective models by considering many complex parameters in performing accurate analysis and anomaly detection (AD) is an important criterion. In this study, machine learning-based AD is performed using a dataset containing meteorological data on different features collected between January 1, 2019, and June 30, 2023, for Kazakhstan, which has the ninth-largest surface area in the world. The Hierarchical Temporal Memory (HTM) model was used for AD, which can provide more accurate forecasts by modeling long-term dependencies and producing effective results in solving TS problems. Detected anomalies are reported at various levels depending on threshold values. In addition, to analyze the ADs more precisely, correlations are calculated using the Spearman model, which allows us to determine the strength and direction of the monotonic relationship between variables. The study's findings show that the HTM is an effective model for AD using TS data on meteorological features.

Kaynakça

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  • Choi K, Yi J., Park C., and Yoon S., “Deep learning for anomaly detection in time-series data: review, analysis, and guidelines,” IEEE Access, vol. 9, pp. 120043–120065, 2021.
  • Längkvist M., Karlsson L., and Loutfi A., “A review of unsupervised feature learning and deep learning for time-series modeling,” Pattern Recognition Letters, vol. 42, no. 1, pp. 11–24, 2014, doi: 10.1016/j.patrec.2014.01.008.
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  • Box G., “Box and Jenkins: Time Series Analysis, Forecasting and Control,” in A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century, Springer, 2013, pp. 161–215.
  • Fu TC, “A review on time series data mining,” Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 164–181, 2011, doi: 10.1016/j.engappai.2010.09.007.
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  • Dietterich TG, “Machine learning for sequential data: A review,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2396, pp. 15–30, doi: 10.1007/3-540-70659-3_2.
  • Agrawal R., Faloutsos C., and Swami A., “Efficient similarity search in sequence databases,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1993, vol. 730 LNCS, pp. 69–84, doi: 10.1007/3-540-57301-1_5.
  • Abonyi J., Feil B., Nemeth S., and Arva P., “Modified Gath-Geva clustering for fuzzy segmentation of multivariate time-series,” Fuzzy Sets and Systems, vol. 149, no. 1, pp. 39–56, 2005, doi: 10.1016/j.fss.2004.07.008.
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  • Keogh E. and Pazzani M., “An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback,” in Kdd, 1998, vol. 98, pp. 239–243, . Available: http://www.aaai.org/Papers/KDD/1998/KDD98-041.pdf.
  • Liu G., Zhong K., Li H., Chen T., and Wang Y., “A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses,” Information Processing in Agriculture, 2022.
  • UA. Bhatti et al., “Time series analysis and forecasting of air pollution particulate matter (PM 2.5): an SARIMA and factor analysis approach,” Ieee Access, vol. 9, pp. 41019–41031, 2021.
  • Saka F., Karaoğlan KM, “Detecting Anomalies in Dam Water Levels using Hierarchical Temporal Memory: A Case Study in Istanbul Province,” in 4th International Symposium of Engineering Applications on Civil Engineering and Earth Sciences 2023 (IEACES2023), 2023, pp. 139–150.
  • Thoppil NM., Vasu V., and Rao CSP., “Deep Learning Algorithms for Machinery Health Prognostics Using Time-Series Data: A Review,” Journal of Vibration Engineering and Technologies, vol. 9, no. 6, pp. 1123–1145, 2021, doi: 10.1007/s42417-021-00286-x.
  • Abanda A., Mori U., and Lozano JA., “A review on distance based time series classification,” Data Mining and Knowledge Discovery, vol. 33, no. 2, pp. 378–412, 2019, doi: 10.1007/s10618-018-0596-4.
  • Li H.and Du T., “Multivariate time-series clustering based on component relationship networks,” Expert Systems with Applications, vol. 173, p. 114649, 2021, doi: 10.1016/j.eswa.2021.114649.
  • Wu J., Zeng W., and Yan F., “Hierarchical Temporal Memory method for time-series-based anomaly detection,” Neurocomputing, vol. 273, pp. 535–546, 2018, doi: 10.1016/j.neucom.2017.08.026.
  • Li G.and Jung JJ., “Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges,” Information Fusion, vol. 91, pp. 93–102, 2023, doi: 10.1016/j.inffus.2022.10.008.
  • Ahmed M., Mahmood AN., and Islam MR., “A survey of anomaly detection techniques in financial domain,” Future Generation Computer Systems, vol. 55, pp. 278–288, 2016.
  • Shaukat K. et al., “A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives,” in Advances in Intelligent Systems and Computing, 2021, vol. 1363 AISC, pp. 865–877, doi: 10.1007/978-3-030-73100-7_60.
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  • Shyu ML., Chen SC., Sarinnapakorn K., and Chang L., “A novel anomaly detection scheme based on principal component classifier,” in Proceedings of the IEEE foundations and new directions of data mining workshop, 2003, pp. 172–179.
  • Angiulli F. and Pizzuti C., “Fast outlier detection in high dimensional spaces,” in European conference on principles of data mining and knowledge discovery, 2002, pp. 15–27.
  • Hosseinzadeh M., Rahmani AM., Vo B., Bidaki M., Masdari M., and Zangakani M., “Improving security using SVM-based anomaly detection: issues and challenges,” Soft Computing, vol. 25, no. 4, pp. 3195–3223, 2021, doi: 10.1007/s00500-020-05373-x.
  • Hu M., Feng X., Ji Z., Yan K., and Zhou S., “A novel computational approach for discord search with local recurrence rates in multivariate time series,” Information Sciences, vol. 477, pp. 220–233, 2019.
  • Chandola V., Banerjee A., and Kumar V., “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009.
  • Lavin A. and Ahmad S., “Evaluating real-time anomaly detection algorithms--the Numenta anomaly benchmark,” in 2015 IEEE 14th international conference on machine learning and applications (ICMLA), 2015, pp. 38–44.
  • Sgueglia A., Di Sorbo A., Visaggio CA., and Canfora G., “A systematic literature review of IoT time series anomaly detection solutions,” Future Generation Computer Systems, vol. 134, pp. 170–186, 2022.
  • Terrades OR., Berenguel A., and Gil D., “A flexible outlier detector based on a topology given by graph communities,” Big Data Research, vol. 29, p. 100332, 2022.
  • Li C., Mo L., Tang H., and Yan R., “Lifelong condition monitoring based on NB-IoT for anomaly detection of machinery equipment,” Procedia Manufacturing, vol. 49, pp. 144–149, 2020, doi: 10.1016/j.promfg.2020.07.010.
  • Kim TY. and Cho SB., “Web traffic anomaly detection using C-LSTM neural networks,” Expert Systems with Applications, vol. 106, pp. 66–76, 2018.
  • He Q., Zheng YJ., Zhang CL., and Wang HY., “MTAD-TF: Multivariate time series anomaly detection using the combination of temporal pattern and feature pattern,” Complexity, vol. 2020, pp. 1–9, 2020.
  • Audibert J., Michiardi P., Guyard F., Marti S., and Zuluaga MA., “Do deep neural networks contribute to multivariate time series anomaly detection?,” Pattern Recognition, vol. 132, p. 108945, 2022.
  • Lindemann B., Maschler B., Sahlab N., and Weyrich M., “A survey on anomaly detection for technical systems using LSTM networks,” Computers in Industry, vol. 131, p. 103498, 2021.
  • Ahmed M., Naser MA., and Hu J., “A survey of network anomaly detection techniques,” Journal of Network and Computer Applications, vol. 60, pp. 19–31, 2016, doi: 10.1016/j.jnca.2015.11.016.
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  • Nassif AB., Talib MA., Nasir Q., and Dakalbab FM., “Machine learning for anomaly detection: A systematic review,” Ieee Access, vol. 9, pp. 78658–78700, 2021.
  • Schmidl S., Wenig P., and Papenbrock T., “Anomaly Detection in Time Series: A Comprehensive Evaluation,” Proceedings of the VLDB Endowment, vol. 15, no. 9, pp. 1779–1797, 2022, doi: 10.14778/3538598.3538602.
  • Dong B. and Wang X., “Comparison deep learning method to traditional methods using for network intrusion detection,” in 2016 8th IEEE international conference on communication software and networks (ICCSN), 2016, pp. 581–585.
  • Kim K. and Aminanto ME., “Deep learning in intrusion detection perspective: Overview and further challenges,” in 2017 International Workshop on Big Data and Information Security (IWBIS), 2017, pp. 5–10.
  • Karatas G., Demir O., and Sahingoz OK., “Deep learning in intrusion detection systems,” in 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), 2018, pp. 113–116.
  • Li J., Izakian H., Pedrycz W., and Jamal I., “Clustering-based anomaly detection in multivariate time series data,” Applied Soft Computing, vol. 100, p. 106919, 2021.
  • Ahmad S., Lavin A., Purdy S., and Agha Z., “Unsupervised real-time anomaly detection for streaming data,” Neurocomputing, vol. 262, pp. 134–147, 2017.
  • Ma T., Zhu Z., Wang L., Wang H., and Ma L., “Anomaly detection for hydropower turbine based on variational modal decomposition and hierarchical temporal memory,” Energy Reports, vol. 8, pp. 1546–1551, 2022, doi: 10.1016/j.egyr.2022.02.286.
  • Soares E., Costa Jr P., Costa B., and Leite D., “Ensemble of evolving data clouds and fuzzy models for weather time series prediction,” Applied Soft Computing, vol. 64, pp. 445–453, 2018.
  • Bamaqa A., Sedky M., Bosakowski T., and Bastaki BB., “Anomaly Detection Using Hierarchical Temporal Memory (HTM) in Crowd Management,” in Proceedings of the 2020 4th International Conference on Cloud and Big Data Computing, 2020, pp. 37–42, doi: 10.1145/3416921.3416940.
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Toplam 81 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Nöral Ağlar
Bölüm MBD
Yazarlar

Kürşat Mustafa Karaoğlan 0000-0001-9830-7622

Oğuz Fındık 0000-0001-5069-6470

Erdal Başaran 0000-0001-8569-2998

Yayımlanma Tarihi 28 Mart 2024
Gönderilme Tarihi 25 Ocak 2024
Kabul Tarihi 27 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 36 Sayı: 1

Kaynak Göster

APA Karaoğlan, K. M., Fındık, O., & Başaran, E. (2024). Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 36(1), 481-498. https://doi.org/10.35234/fumbd.1425635
AMA Karaoğlan KM, Fındık O, Başaran E. Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Mart 2024;36(1):481-498. doi:10.35234/fumbd.1425635
Chicago Karaoğlan, Kürşat Mustafa, Oğuz Fındık, ve Erdal Başaran. “Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36, sy. 1 (Mart 2024): 481-98. https://doi.org/10.35234/fumbd.1425635.
EndNote Karaoğlan KM, Fındık O, Başaran E (01 Mart 2024) Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36 1 481–498.
IEEE K. M. Karaoğlan, O. Fındık, ve E. Başaran, “Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 36, sy. 1, ss. 481–498, 2024, doi: 10.35234/fumbd.1425635.
ISNAD Karaoğlan, Kürşat Mustafa vd. “Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36/1 (Mart 2024), 481-498. https://doi.org/10.35234/fumbd.1425635.
JAMA Karaoğlan KM, Fındık O, Başaran E. Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2024;36:481–498.
MLA Karaoğlan, Kürşat Mustafa vd. “Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 36, sy. 1, 2024, ss. 481-98, doi:10.35234/fumbd.1425635.
Vancouver Karaoğlan KM, Fındık O, Başaran E. Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2024;36(1):481-98.