no code implementations • 12 Jan 2024 • Subina Khanal, Seshu Tirupathi, Giulio Zizzo, Ambrish Rawat, Torben Bach Pedersen
To address these limitations, in this paper, we pre-train the time series Transformer model on a source domain with sufficient data and fine-tune it on the target domain with limited data.
no code implementations • 10 Sep 2022 • Yan Zhao, Liwei Deng, Xuanhao Chen, Chenjuan Guo, Bin Yang, Tung Kieu, Feiteng Huang, Torben Bach Pedersen, Kai Zheng, Christian S. Jensen
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energy management, where anomaly detection is often essential to enable reliability and safety.
1 code implementation • 20 Nov 2019 • Suela Isaj, Torben Bach Pedersen, Esteban Zimányi
Besides the traditional cartographic data sources, spatial information can also be derived from location-based sources.
Databases
1 code implementation • 25 Mar 2019 • Søren Kejser Jensen, Torben Bach Pedersen, Christian Thomsen
We present the first MMGC method GOLEMM and extend model types to compress time series groups.
Databases
no code implementations • 9 May 2018 • Davide Frazzetto, Bijay Neupane, Torben Bach Pedersen, Thomas Dyhre Nielsen
First, DR schemes are highly demanding for the users, as users need to provide direct information, e. g. via surveys, on their energy consumption preferences.
no code implementations • 2 May 2018 • Bijay Neupane, Torben Bach Pedersen, Bo Thiesson
In a typical device-level flexibility forecast, a market player is more concerned with the \textit{utility} that the demand flexibility brings to the market, rather than the intrinsic forecast accuracy.
2 code implementations • 3 Oct 2017 • Søren Kejser Jensen, Torben Bach Pedersen, Christian Thomsen
The collection of time series data increases as more monitoring and automation are being deployed.
Databases G.1.2; D.2.11; E.4; E.2; E.1; H.2; H.2.4; C.2.4; H.2.8; G.3