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 [1].
Imaging Time-Series to Improve Classification and Imputation
Authors: Zhiguang Wang, Tim Oates
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate our approaches achieve the best performance on 9 of 20 standard dataset compared with 9 previous and current best classification methods. The imputation MSE on test data is reduced by 12.18%-48.02% compared to using the raw data. |
| Researcher Affiliation | Academia | Zhiguang Wang and Tim Oates Department of Computer Science and Electric Engineering University of Maryland, Baltimore County EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We apply Tiled CNNs to classify time series using GAF and MTF representations on 20 datasets from [Keogh et al., 2011] |
| Dataset Splits | Yes | The datasets are pre-split into training and testing sets to facilitate experimental comparisons. we use a linear soft margin SVM [Fan et al., 2008] and select C by 5-fold cross validation over {10 4, 10 3, . . . , 104} on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models) used for running its experiments. |
| Software Dependencies | No | The paper mentions "Theano [Bastien et al., 2012]" but does not specify its version number, nor does it list other software dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, the size of the GAF image is regulated by the the number of PAA bins SGAF. The Tiled CNN is trained with image size {SGAF , SMT F } {16, 24, 32, 40, 48} and quantile size Q {8, 16, 32, 64}. At the last layer of the Tiled CNN, we use a linear soft margin SVM [Fan et al., 2008] and select C by 5-fold cross validation over {10 4, 10 3, . . . , 104} on the training set. For the DA models we use batch gradient descent with a batch size of 20. Optimization iterations run until the MSE changed less than a threshold of 10 3 for GASF and 10 5 for raw time series. A single hidden layer has 500 hidden neurons with sigmoid functions. we randomly set 20% of the raw data among a specific time series to be zero (salt-and-pepper noise). |