SourceTowards Neural Scaling Laws for Time Series Foundation Models
The paper retrains Moirai and a simpler encoder-only transformer on the same 16.8B time-point corpus and compares their scaling behaviour across parameter counts. On in-distribution data, Moirai's design choices yield better forecasting performance. On out-of-distribution data, however, as the number of parameters increases, Moirai is gradually surpassed by the simpler baseline, and its fitted power-law slope is smaller, indicating weaker scalability. The authors attribute this to Moirai's architectural modifications, which primarily benefit in-distribution performance without transferring to out-of-distribution generalisation.