Unsupervised Feature Learning from Time Series
Authors: Qin Zhang, Jia Wu, Hong Yang, Yingjie Tian, Chengqi Zhang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that USLM outperforms search-based algorithms on real-world time series data. |
| Researcher Affiliation | Collaboration | Quantum Computation & Intelligent Systems Centre, University of Technology Sydney, Australia Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, China. Key Lab of Big Data Mining & Knowledge Management, Chinese Academy of Sciences, Beijing, China. Math Works, Beijing, China |
| Pseudocode | Yes | Algorithm 1. Unsupervised Shapelet Learning Algorithm (USLA) 1: Input: Time series T with c classes Length and number of shapelets: lmin, r, k Number of internal iterations imax Learning rate and Parameters λ1, λ2, λ3 and , σ 2: Output: Shapelets S and class labels Y 3: Initialize: S0, W0, Y0 4: While Not convergent do 5: Calculate: Xt(T, St| ), LGt(T, St| , σ) 6: and Ht(St| ) based on Eqs. (2), (4), and (6); 7: update Wt+1, Yt+1: 8: Yt+1 λ2WT t Xt(LGt + λ2I) 1 9: Wt+1 (λ2Xt XT t + λ3I) 1(λ2Xt YT t+1). 10: update St+1: 11: for i = 1, . . . , imax do 12: Si+1 Si r Si 13: r Si = @F(Si|Xt+1,Yt+1) @S is from Eq. (17) 14: end for 15: St+1 = Simax+1 16: t t + 1 17: end while 18: Output: S = St; Y = Yt; W = Wt. |
| Open Source Code | Yes | The Matlab source codes and data are available online1. 1https://github.com/Blind Review/shapelet |
| Open Datasets | Yes | We use seven time series benchmark datasets download from the UCR time series archive [Chen et al., 2015] [Cetin et al., 2015]. The UCR time series classification archive, www.cs.ucr.edu/ eamonn/time series data/. 2015. |
| Dataset Splits | No | Table 1 lists "Train/Test" splits for the datasets (e.g., CBF 30/900(930)), but there is no explicit mention or description of a validation dataset split. |
| Hardware Specification | Yes | All experiments are conducted on a Windows 8 machine with 3.00GHz CPU and 8GB memory. |
| Software Dependencies | No | The paper mentions "Matlab source codes" but does not specify the version of Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The remaining parameters are fixed as follows, λ1 = λ2 = λ3 = λ4 = 1, σ = 1, Imax = 50, = 0.01 and the length of the shapelets is set to 10. |