most citedUnsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Robust Feasible Route Construction through Collaborative Partition Optimization

Oguzhan Karaahmetoglu, Hyong Kim

Large-scale Capacitated Vehicle Routing Problems (CVRPs) are commonly solved by partitioning customers into smaller routing problems that can be optimized independently. While this…

cs.AI2026

Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing

Oguzhan Karaahmetoglu, Hyong Kim

Large-scale capacitated vehicle routing problems (CVRPs) are commonly addressed using cluster-first route-second (CFRS) approaches that split a routing instance into smaller, compu…

cs.LG2020

Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments

Fatih Ilhan, Oguzhan Karaahmetoglu, Ismail Balaban +1

We investigate nonlinear regression for nonstationary sequential data. In most real-life applications such as business domains including finance, retail, energy and economy, timese…

stat.ML2020

Spatio-temporal Sequence Prediction with Point Processes and Self-organizing Decision Trees

Oguzhan Karaahmetoglu, Suleyman S. Kozat

We study the spatio-temporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatio-temporal prediction is extensively studied in Machine Learnin…

stat.ML2020★ 4 cited

Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks

Oguzhan Karaahmetoglu, Fatih Ilhan, Ismail Balaban +1

We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. Our algorithm is fully unsuper…

stat.ML2020

Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees

Oguzhan Karaahmetoglu, Suleyman Serdar Kozat

We study the spatio-temporal prediction problem, which has attracted the attention of many researchers due to its critical real-life applications. In particular, we introduce a nov…