37 citations · 43 across the 24 of their papers we have counts for
7 papers · 1 filter
EasyTime: Time Series Forecasting Made Easy
Xiangfei Qiu, Xiuwen Li, Ruiyang Pang +11
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researche…
Assessing Pre-Trained Models for Transfer Learning Through Distribution of Spectral Components
Tengxue Zhang, Yang Shu, Xinyang Chen +3
Pre-trained model assessment for transfer learning aims to identify the optimal candidate for the downstream tasks from a model hub, without the need of time-consuming fine-tuning.…
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
Shiyan Hu, Kai Zhao, Xiangfei Qiu +4
Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predictin…
TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting
Zhe Li, Xiangfei Qiu, Peng Chen +8
Time Series Forecasting (TSF) is key functionality in numerous fields, such as financial investment, weather services, and energy management. Although increasingly capable TSF meth…
Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfer
Yihang Wang, Yuying Qiu, Peng Chen +6
With the growing availability of multi-domain time series data, there is an increasing demand for general forecasting models pre-trained on multi-source datasets to support diverse…
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
Qichao Shentu, Beibu Li, Kai Zhao +5
Time series anomaly detection plays a vital role in a wide range of applications. Existing methods require training one specific model for each dataset, which exhibits limited gene…
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Peng Chen, Yingying Zhang, Yunyao Cheng +5
Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales.…