Website of T. Moudiki
benchmarks
| Source: Mastodon | Original article
Research extends Theta forecasting to new models, benchmarking on large datasets.
Thierry Moudiki's webpage has been updated with new research extending the Theta forecasting method to various models, including GLMs, GAMs, GLMBOOST, and attention. This development is significant as it benchmarks the performance of these models on large datasets from the Tourism, M1, M3, and M4 competitions, comprising 28,000 series.
This matters because advancements in forecasting methods can greatly impact fields like data science and machine learning, where accurate predictions are crucial. Moudiki's work, available on his personal webpage and GitHub, demonstrates his ongoing contributions to the tech and statistical communities.
As we follow Moudiki's research, it will be interesting to watch how his extensions to the Theta forecasting method are received and applied in various industries, potentially leading to more accurate forecasting and decision-making. With his background in engineering, statistics, and data science, Moudiki's work is likely to continue pushing boundaries in machine learning and numerical optimization.
Sources
Back to AIPULSEN