2 citations · 2 across the 2 of their papers we have counts for
5 papers · 1 filter
Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping
Daesoo Lee, Sara Malacarne, Erlend Aune
In this paper, we introduce Neural Mapper for Vector Quantized Time Series Generator (NM-VQTSG), a novel method aimed at addressing fidelity challenges in vector quantized (VQ) tim…
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation
Johan Vik Mathisen, Erlend Lokna, Daesoo Lee +1
State-of-the-art approaches in time series generation (TSG), such as TimeVQVAE, utilize vector quantization-based tokenization to effectively model complex distributions of time se…
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling
Daesoo Lee, Sara Malacarne, Erlend Aune
We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE…
Masked Generative Modeling with Enhanced Sampling Scheme
Daesoo Lee, Erlend Aune, Sara Malacarne
This paper presents a novel sampling scheme for masked non-autoregressive generative modeling. We identify the limitations of TimeVQVAE, MaskGIT, and Token-Critic in their sampling…
Vector Quantized Time Series Generation with a Bidirectional Prior Model
Daesoo Lee, Sara Malacarne, Erlend Aune
Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants. However, the fu…