484 citations · 602 across the 7 of their papers we have counts for
7 papers
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Xiaomin Song, Qingsong Wen, Yan Li +1
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singul…
Time Series Data Augmentation for Deep Learning: A Survey
Qingsong Wen, Liang Sun, Fan Yang +4
Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training da…
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
Jingkun Gao, Xiaomin Song, Qingsong Wen +3
The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we…
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Qingsong Wen, Jingkun Gao, Xiaomin Song +2
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection. Besides noise signal, time series can contain no…
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
Ming Lin, Xiaomin Song, Qi Qian +4
Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In…
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
Ming Lin, Shuang Qiu, Jieping Ye +5
Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is…
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Qingsong Wen, Jingkun Gao, Xiaomin Song +3
Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerou…