Weather-Adaptive Multi-Step Forecasting of State of Polarization Changes in Aerial Fibers Using Wavelet Neural Networks
We introduce a novel weather-adaptive approach for multi-step forecasting of multi-scale SOP changes in aerial fiber links. By harnessing the discrete wavelet transform and incorporating weather data, our approach improves forecasting accuracy by over 65% in RMSE and 63% in MAPE compared to baselines.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting
Weather forecasting is a fundamental task in spatiotemporal data analysis, with broad applications across a wide range of domains. Existing data-driven forecasting methods typically model atmospheric dynamics over a fixe…
Reinforcement LearningWeather ForecastingAGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting
Accurate weather forecasting is more than grid-wise regression: it must preserve coherent synoptic structures and physical consistency of meteorological fields, especially under autoregressive rollouts where small one-st…
Weather ForecastingModulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts
Weather and climate data are often available at limited temporal resolution, either due to storage limitations, or in the case of weather forecast models based on deep learning, their inherently long time steps. The coar…
Weather ForecastingAdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors. But no model consistently dominates spatio-temporally, and relative …
Weather ForecastingWeather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
Weather forecasting is one of the cornerstones of meteorological work. In this paper, we present a new benchmark dataset named Weather2K, which aims to make up for the deficiencies of existing weather forecasting dataset…
Spatio-Temporal ForecastingTime Series AnalysisTime Series ForecastingWeather Forecasting