We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| eeci/annex_37 | pwc_unofficial | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| MAE | Ran | eeci/annex_37/assess_forecasts.py code served (permissive licence) · get_code("86e750c7345818ce") |
| RMSE | Ran | eeci/annex_37/assess_forecasts.py code served (permissive licence) · get_code("200be6d2c9f9dfff") |
| compute_metric_score | Ran | eeci/annex_37/assess_forecasts.py code served (permissive licence) · get_code("4ac9b925d029f608") |
| get_mparams | Ran | eeci/annex_37/experiments/train_DMS_models.py code served (permissive licence) · get_code("f770529f1970f1c0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two common challenges afflicting the task are the volatility of the predictions and their computational complexity. We introduce N-HiTS, a model which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. These techniques enable the proposed method to assemble its predictions sequentially, emphasizing components with different frequencies and scales while decomposing the input signal and synthesizing the forecast. We prove that the hierarchical interpolation technique can efficiently approximate arbitrarily long horizons in the presence of smoothness. Additionally, we conduct extensive large-scale dataset experiments from the long-horizon forecasting literature, demonstrating the advantages of our method over the state-of-the-art methods, where N-HiTS provides an average accuracy improvement of almost 20% over the latest Transformer architectures while reducing the computation time by an order of magnitude (50 times). Our code is available at bit.ly/3VA5DoT
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.12886")
get_code_for_paper("2201.12886")
have("2201.12886")
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