Search Results for author:
Found 6 papers, 2 papers with code
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
