Research map / Time series / Foundation models for time series
Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train N-BEATS on M4 and transfer without retraining | N-BEATS · M3 | sMAPE: 12.60 to 12.44 (1.2%) | Helped |
| Train N-BEATS on M4 and transfer without retraining | N-BEATS · Tourism | MAPE: 20.40 to 18.82 (7.8%) | Helped |
| Use multiple shared-weight blocks instead of a single block (ablation) | N-BEATS · M3 | sMAPE: 12.60 to 12.44 (1.2%) | Helped |
| Use multiple shared-weight blocks instead of a single block (ablation) | N-BEATS · Tourism | MAPE: 20.40 to 18.82 (7.8%) | Helped |
| Use multiple blocks with unique weights instead of a single block (ablation) | N-BEATS · M3 | sMAPE: 12.60 to 12.40 (1.6%) | Helped |
| Use multiple blocks with unique weights instead of a single block (ablation) | N-BEATS · Tourism | MAPE: 20.40 to 18.91 (7.4%) | Helped |
| Increase shared-weight block count beyond about 30 (ablation) | N-BEATS · M3 and Tourism | forecast error | Hurt |
| Use unique block weights rather than shared weights (ablation) | N-BEATS · Zero-shot forecasting datasets | forecast error | Mixed |
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