RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Authors: Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu5409-5416
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions. |
| Researcher Affiliation | Industry | Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu Machine Intelligence Technology, Alibaba Group Bellevue, Washington 98004, USA {qingsong.wen, jingkun.g, xiaomin.song, liang.sun, huan.xu, shenghuo.zhu}@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1 Robust STL Algorithm Summary |
| Open Source Code | No | The paper mentions implementing their algorithm in Python and using CVXOPT, but does not provide a link or statement for their own source code. |
| Open Datasets | Yes | One is the supermarket and grocery stores turnover from 2000 to 2009 (Alexandrov et al. 2012) |
| Dataset Splits | No | The paper mentions that parameters for baseline algorithms are optimized using cross-validation, but does not specify exact split percentages, sample counts, or detailed splitting methodology. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running experiments were mentioned. |
| Software Dependencies | No | The paper mentions using Python and the CVXOPT library, as well as R packages forecast and stR, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For our proposed Robust STL algorithm, we set the regularization coefficients λ1 = 10, λ2 = 0.5 to control the signal smoothness in the trend extraction, and set the neighborhood parameters K = 2, H = 5 in the seasonality extraction to handle the seasonality shift. |