Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Authors: Ziqiang Cheng, Yang Yang, Wei Wang, Wenjie Hu, Yueting Zhuang, Guojie Song3617-3624
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate whether the representations obtained in this way can be applied effectively in various scenarios, we conduct experiments based on three public time series datasets, and two real-world datasets from different domains. Experimental results clearly show the improvements achieved by our approach compared with 16 state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | College of Computer Science and Technology, Zhejiang University State Grid Huzhou Power Supply Co. Ltd, China Key Laboratory of Machine Perception, Ministry of Education, Peking University |
| Pseudocode | Yes | Algorithm 1 Shapelet Evolution Graph Construction; Algorithm 2 Time Series Embedding Framework |
| Open Source Code | Yes | the source codes, along with the implementation details, parameter settings and documentations, can be found on the project homepage: https://petecheng.github.io/Time2Graph. |
| Open Datasets | Yes | We use three public datasets, Earthquakes (EQS), Worms Two Class (WTC) and Strawberry (STB) from the UCR Time Series Archive (Dau et al. 2018) |
| Dataset Splits | Yes | We choose XGBoost (Chen and Guestrin 2016) as the outer classifier, and use 5-fold nested cross-validation to conduct fine-tuning on hyper-parameters. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "XGBoost" but does not specify version numbers for XGBoost or any other software dependencies. |
| Experiment Setup | Yes | We examine the sensitivities of three important hyperparameters: number of selected shapelets K, graph embedding size B and segment length l. |