arXiv Open Access 2025

Forecasting Arctic Temperatures with Temporally Dependent Data Using Quantile Gradient Boosting and Adaptive Conformal Prediction Regions

Richard Berk

Lihat Sumber

Abstrak

Using data from the Longyearbyen weather station, quantile gradient boosting (``small AI'') is applied to forecast daily 2023 temperatures in Svalbard, Norway. The 0.60 quantile loss weights underestimates about 1.5 times more than overestimates. Predictors include five routinely collected indicators of weather conditions, each lagged by 14~days, yielding temperature forecasts with a two-week lead time. Conformal prediction regions quantify forecasting uncertainty with provably valid coverage. Forecast accuracy is evaluated with attention to local stakeholder concerns, and implications for Arctic adaptation policy are discussed.

Topik & Kata Kunci

stat.AP

Penulis (1)

Richard Berk

Format Sitasi

APA MLA BibTeX

Berk, R. (2025). Forecasting Arctic Temperatures with Temporally Dependent Data Using Quantile Gradient Boosting and Adaptive Conformal Prediction Regions. https://arxiv.org/abs/2510.23976

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓