Search Results for author:
Found 7 papers, 2 papers with code
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Rather than directly dismissing the role of incremental learning, we ascribe this lack of anticipated performance improvement to the mismatch between the LLM4Recarchitecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendation, hampering its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context.
FITS: Modeling Time Series with $10k$ Parameters
In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis.
FrAug: Frequency Domain Augmentation for Time Series Forecasting
In time series forecasting (TSF), we need to model the fine-grained temporal relationship within time series segments to generate accurate forecasting results given data in a look-back window.
Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognition
By exploiting the rolling shutter of CMOS sensors, we manage to inject a color stripe overlapped on the traffic light in the image, which can cause a red light to be recognized as a green light or vice versa.
DeepFIB: Self-Imputation for Time Series Anomaly Detection
Due to the inherently unpredictable and highly varied nature of anomalies and the lack of anomaly labels in historical data, the AD problem is typically formulated as an unsupervised learning problem.
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.
Ranked #1 on
Time Series Forecasting
on ETTh1 (24) Multivariate
(using extra training data)
