Now showing items 1-2 of 2

  • A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data 

    Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a ...

  • Joint Pricing and Ordering Problem with Charitable Donations 

    Authors:Çavdaroğlu, Nur Ayvaz
    Publisher and Date:(Mdpi, 2020)
    Finding the correct pricing strategy for a product with multiple versions is an issue for retailers from various industries. In this paper, joint pricing and ordering problem is considered for a product that has two versions at each selling period. Two models, namely with or without the donation option, are analyzed and optimality conditions and monotonicity properties of the decision variables are characterized. When demands of products depend on prices of both versions, donating part of old ...