We lifted 19 functions out of this paper's own repositories and ran 14 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| uchidalab/time_series_augmentation | canonical | 14 of 19 |
| Function | Status | Where it lives |
|---|---|---|
| border_selection | Ran | uchidalab/time_series_augmentation/utils/prototype_selection.py code served (permissive licence) · get_code("0f0f7c721707eed3") |
| center_selection | Ran | uchidalab/time_series_augmentation/utils/prototype_selection.py code served (permissive licence) · get_code("dbf36d6824e2a63d") |
| cnn_lenet | Ran | uchidalab/time_series_augmentation/utils/models.py code served (permissive licence) · get_code("fe95b56755997e58") |
| compute_CD | Ran | uchidalab/time_series_augmentation/utils/nemenyi.py code served (permissive licence) · get_code("87f38bda64fde768") |
| get_datasets | Ran | uchidalab/time_series_augmentation/utils/input_data.py code served (permissive licence) · get_code("e381033c0b9d40ec") |
| jitter | Ran | uchidalab/time_series_augmentation/utils/augmentation.py code served (permissive licence) · get_code("7e91f7d6b90183d5") |
| load_data_from_file | Ran | uchidalab/time_series_augmentation/utils/input_data.py code served (permissive licence) · get_code("7cf31f6b78c606a2") |
| mlp4 | Ran | uchidalab/time_series_augmentation/utils/models.py code served (permissive licence) · get_code("0297da83a1396658") |
| nb_classes | Ran | uchidalab/time_series_augmentation/utils/datasets.py code served (permissive licence) · get_code("3213e921153b8366") |
| nb_dims | Ran | uchidalab/time_series_augmentation/utils/datasets.py code served (permissive licence) · get_code("343f559e28587141") |
| random_selection | Ran | uchidalab/time_series_augmentation/utils/prototype_selection.py code served (permissive licence) · get_code("43dc9c195316089e") |
| rotation | Ran | uchidalab/time_series_augmentation/utils/augmentation.py code served (permissive licence) · get_code("ec568a4fd5c0ca0c") |
| scaling | Ran | uchidalab/time_series_augmentation/utils/augmentation.py code served (permissive licence) · get_code("570f35b7d04be7f7") |
| shape_dtw | Ran | uchidalab/time_series_augmentation/utils/dtw.py code served (permissive licence) · get_code("4967a8f159950811") |
| class_offset | Not yet run | uchidalab/time_series_augmentation/utils/datasets.py code served (permissive licence) · get_code("03a4fd381f7f635d") |
| dtw | Not yet run | uchidalab/time_series_augmentation/utils/dtw.py code served (permissive licence) · get_code("66037d9834a6eeb5") |
| get_model | Not yet run | uchidalab/time_series_augmentation/utils/models.py code served (permissive licence) · get_code("4c6299e6a59d9118") |
| graph_ranks | Not yet run | uchidalab/time_series_augmentation/utils/nemenyi.py code served (permissive licence) · get_code("bb052203a1f1e8df") |
| read_data_sets | Not yet run | uchidalab/time_series_augmentation/utils/input_data.py code served (permissive licence) · get_code("0dfcb65b2964f827") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.15951")
get_code_for_paper("2007.15951")
have("2007.15951")
Connect an agent — have() is free.