Browsing by Author "Alagoz, S. M."

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    Forecasting multivariate time-series data using LSTM and mini-batches
    (Springer, 2020) Khodabakhsh, Athar; Arı, İsmail; Bakır, M.; Alagoz, S. M.; Computer Science; Bohlouli, M.; Bigham, B. S.; Narimani, Z.; Vasighi, M.; Ansari, E.; ARI, Ismail; Khodabakhsh, Athar
    Multivariate time-series data forecasting is a challenging task due to nonlinear interdependencies in complex industrial systems. It is crucial to model these dependencies automatically using the ability of neural networks to learn features by extraction of spatial relationships. In this paper, we converted non-spatial multivariate time-series data into a time-space format and used Recurrent Neural Networks (RNNs) which are building blocks of Long Short-Term Memory (LSTM) networks for sequential analysis of multi-attribute industrial data for future predictions. We compared the effect of mini-batch length and attribute numbers on prediction accuracy and found the importance of spatio-temporal locality for detecting patterns using LSTM.
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    Stream analytics and adaptive windows for operational mode identification of time-varying industrial systems
    (IEEE, 2018-09-07) Khodabakhsh, Athar; Arı, İsmail; Bakır, M.; Alagoz, S. M.; Computer Science; ARI, Ismail; Khodabakhsh, Athar
    It is necessary to develop accurate, yet simple and efficient models that can be used with high-speed industrial data streams. In this paper, we develop a mode identification technique using stream analytics and show that it may be more effective than batch models, especially for time-varying systems. These industrial systems continuously monitor hundreds of sensors, but the relationships among variables change over time, which are identified as different operational modes. To detect drifts among modes, predictive modeling techniques such as regression analysis, K-means and DBSCAN clustering are used over sensor data streams from an oil refinery and models are updated in real-time using window-based analysis. Finally, an adaptive window size tuning approach based on the TCP congestion control algorithm is discussed, which reduces model update costs as well as prediction errors.