3 citations · 3 across the 1 of their papers we have counts for
3 papers
cs.LG2022
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting
Liwang Zhou, Jing Gao
Feature engineering is required to obtain better results for time series forecasting, and decomposition is a crucial one. One decomposition approach often cannot be used for numero…
cs.LG2019★ 3 cited
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…
cs.LG2018
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…