Adaptable Regression Method for Ensemble Consensus Forecasting
Authors: John Williams, Peter Neilley, Joseph Koval, Jeff McDonald
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm is illustrated for 0-72 hour temperature forecasts at over 1200 sites in the contiguous U.S. based on a 22-member forecast ensemble, and its performance over multiple seasons is compared to a state-of-the-art ensemble-based forecasting system. |
| Researcher Affiliation | Industry | The Weather Company, Andover, MA john.williams@weather.com |
| Pseudocode | No | The paper describes the methodology using mathematical equations and steps, but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing code or a link to a code repository. |
| Open Datasets | No | The paper mentions using "Surface temperature measurements from over 1200 ground weather station ( METAR ) locations" and "hourly temperature forecasts from an ensemble of 22 input forecasts", but does not provide concrete access information (link, DOI, formal citation for public dataset) for this data. |
| Dataset Splits | No | The paper mentions "Cross-validation was not appropriate for this evaluation" and "experiments for determining good parameters were performed using a small number of odd-hour forecast lead times", but does not provide specific dataset split information (e.g., exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing subsets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "MATLAB s quadprog function" and "MATLAB s linsolve", but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For these AR results, the bias modulation = 1 or 0.8, regularization parameter = 0 or 0.1, and error covariance aggregation proportion = 0 or 0.7; the bias aggregation proportion = 0.0 is fixed for all four. In this and all other AR runs shown in this paper, the bias and error covariance learning rates were fixed at and ; the goal weight ; and the weight limits were and . |