This repository contains the extracted time series features (tsfeatures) for each variate and the detailed forecasting results for every experiment.
Note: These files are for building leaderboard and visualization; users do not need to download this directory.
features/: Statistical Features (tsfeatures)
Each dataset's features are saved to: output/features/{dataset}/{freq}/.
This directory stores the computed tsfeatures for the variates in the dataset. The folder contains a CSV file named either:
test.csv: Features calculated on the test split.full.csv: Features calculated on the full dataset.results/: Experimental Results
Each experiment is saved to: output/results/{model}/{dataset}/{freq}/{horizon}/.
Each directory represents a specific model-dataset-frequency-horizon combination and contains the following files:
config.json: The experiment settings and hyperparameters.metrics.npz: Window-level forecasting metrics (e.g., MASE, CRPS).predictions.npz: Quantile prediction results for each sliding window.This repository contains the extracted time series features (tsfeatures) for each variate and the detailed forecasting results for every experiment.
Note: These files are for building leaderboard and visualization; users do not need to download this directory.
features/: Statistical Features (tsfeatures)
Each dataset's features are saved to: output/features/{dataset}/{freq}/.
This directory stores the computed tsfeatures for the variates in the dataset. The folder contains a CSV file named either:
test.csv: Features calculated on the test split.full.csv: Features calculated on the full dataset.results/: Experimental Results
Each experiment is saved to: output/results/{model}/{dataset}/{freq}/{horizon}/.
Each directory represents a specific model-dataset-frequency-horizon combination and contains the following files:
config.json: The experiment settings and hyperparameters.metrics.npz: Window-level forecasting metrics (e.g., MASE, CRPS).predictions.npz: Quantile prediction results for each sliding window.