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..
Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest
Authors: Xiaosheng Li, Jessica Lin, Liang Zhao
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed algorithm extensively on all 85 datasets from the well-known UCR time series archive, and compare with the state-of-the-art approaches with statistical analysis. |
| Researcher Affiliation | Academia | 1Department of Computer Science, George Mason University, USA 2Department of Information Science and Technology, George Mason University, USA |
| Pseudocode | Yes | Algorithm 1 gives the pseudo-code of SPF. |
| Open Source Code | Yes | The C++ source code of SPF is available in the supplementary material1. 1http://mason.gmu.edu/~xli22/SPF |
| Open Datasets | Yes | To evaluate the proposed algorithm, we run it on all 85 datasets from the UCR time series archive [Chen et al., 2015]. This public archive contains different types of labeled time series from various fields. |
| Dataset Splits | No | Each dataset in the archive contains a training set and a testing set. We fuse both sets and use all the data in the experiment. |
| Hardware Specification | Yes | A single core of AMD Opteron Processor 6276 (2299 MHz) and 16 GB memory are used. |
| Software Dependencies | No | The C++ source code of SPF is available in the supplementary material1. In our implementation, we use Metis [Karypis and Kumar, 1998] to partition the graph. The source code of k-shape and k-means are obtained from the author of [Paparrizos and Gravano, 2015] and the code is in Matlab. (No specific version numbers for C++, Metis, or Matlab are provided.) |
| Experiment Setup | Yes | The number of iterations of k-shape and kmeans are set to 100... The ensemble size of SPF is set to 100 in all the experiments. k is set to equal the number of classes of the dataset in use. ...In SPF, γ is set to 4... ω takes the values from wd = {3, 4, 5, 6, 7} and l takes the values from wl = {0.025, 0.05, 0.075, . . . , 1}m... we set the lower bound to 0.25 n/k... The upper bound is set to n 0.25 n/k. |