Search Results for author: Cristian Lumezanu

Found 4 papers, 2 papers with code

Deep Federated Anomaly Detection for Multivariate Time Series Data

no code implementations9 May 2022 Wei Zhu, Dongjin Song, Yuncong Chen, Wei Cheng, Bo Zong, Takehiko Mizoguchi, Cristian Lumezanu, Haifeng Chen, Jiebo Luo

Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device.

Federated Learning Time Series +1

Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series

no code implementations24 Jan 2022 Jurijs Nazarovs, Cristian Lumezanu, Qianying Ren, Yuncong Chen, Takehiko Mizoguchi, Dongjin Song, Haifeng Chen

In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i. e., during testing we can prescribe classes that are missing during training.

Time Series Time Series Classification

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

5 code implementations20 Nov 2018 Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

Time Series Unsupervised Anomaly Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.