LOTSA

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Findings

• IC-537Moirai's architectural enhancements (any-variate attention, multi-scale patch embedding, diverse mixture distribution) improve in-distribution forecasting but reduce out-of-distribution scalability relative to a simpler encoder-only baseline [train]
• IC-538Chronos-T5's discrete probability prediction approach yields very small power-law exponents on NLL, limiting its scalability, and its in-distribution gains do not extend to out-of-distribution data [train]