B Prakash, Harshavardhan Kamarthi
We lifted 10 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| adityalab/samay | canonical | 9 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| MAE | Ran | adityalab/samay/src/samay/metric.py code served (permissive licence) · get_code("3d45af2dc19af0af") |
| MASE | Ran | adityalab/samay/src/samay/metric.py code served (permissive licence) · get_code("ab4f6a5fa72f667b") |
| MSE | Ran | adityalab/samay/src/samay/metric.py code served (permissive licence) · get_code("bddb4a39908fde27") |
| filter_dict | Ran | adityalab/samay/src/samay/moirai_utils.py code served (permissive licence) · get_code("79566c6a8f0aad1a") |
| freq_mapping | Ran | adityalab/samay/src/samay/dataset.py code served (permissive licence) · get_code("1a96712a90c9bc00") |
| get_multivariate_data | Ran | adityalab/samay/src/samay/utils.py code served (permissive licence) · get_code("53b5bc00f1f8e4f8") |
| handle_distr_output | Ran | adityalab/samay/src/samay/moirai_utils.py code served (permissive licence) · get_code("af458dcb2674cce8") |
| load_args | Ran | adityalab/samay/src/samay/utils.py code served (permissive licence) · get_code("b51cc76a36acc599") |
| read_yaml | Ran | adityalab/samay/src/samay/utils.py code served (permissive licence) · get_code("cba288293510660e") |
| quantize_linear_layers | Not yet run | adityalab/samay/src/samay/quantization.py code served (permissive licence) · get_code("c9d2804d2f08ed7e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large pre-trained models have been vital in recent advancements in domains like language and vision, making model training for individual downstream tasks more efficient and provide superior performance. However, tackling time-series analysis tasks usually involves designing and training a separate model from scratch leveraging training data and domain expertise specific to the task. We tackle a significant challenge for pre-training a foundational time-series model from multidomain time-series datasets: extracting semantically useful tokenized inputs to the model across heterogenous time-series from different domains. We propose Large Pre-trained Time-series Models (LPTM) that introduces a novel method of adaptive segmentation that automatically identifies optimal dataset-specific segmentation strategy during pre-training. This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.11413")
get_code_for_paper("2311.11413")
have("2311.11413")
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