no code implementations • 17 Apr 2024 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Mahsa Salehi
In this paper, we propose a novel Domain Adaptation Contrastive learning for Anomaly Detection in multivariate time series (DACAD) model to address this issue by combining UDA and contrastive representation learning.
no code implementations • 18 Aug 2023 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
1 code implementation • 9 Nov 2022 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.
1 code implementation • 6 Nov 2021 • Zahra Zamanzadeh Darban, Mohammad Hadi Valipour
While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches.
Ranked #1 on Movie Recommendation on MovieLens 1M (RMSE metric)