Forecasting Temporally Aggregated Time Series
This R package features the essential functions from Neubauer and Filzmoser (2024). Exported functions are
fit_agg_arima: Fit fixed or automatically selected ARIMA models to a temporal hierarchy.reconcile_forecasts: Reconcile base forecasts with reconciliation method of choice.reconcile_forecasts.cv: Time-Series Cross-Validation function reconciliation methods with hyperparameters.forecast_and_reconcile: Combines fitting ARIMA models and reconciling resulting base forecasts.
# install.packages("remotes")
remotes::install_github("neubluk/FTATS")Data examples are available in demo/data_example.R, demo/data_example2.R, demo/data_examples_all.R, as well as simulation studies in demo/simulation.R
Four datasets are available.
- Daily Food Demand Data from Schrankel GmbH (
food_demand_daily) - Australian Electricity Generation (
nem_generation_by_source), taken from Panagiotelis, A., Gamakumara, P., Athanasopoulos, G., and Hyndman, R. J. (2023). Probabilistic forecast reconciliation: Properties, evaluation and score optimisation. European Journal of Operational Research, 306(2):693–706. - Prison Population (
prison_population), taken from Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. - Nightly Visitors in Australia (
VNdata), taken from Wickramasuriya, S. L., Athanasopoulos, G., and Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526):804–819. and Girolimetto, D., Athanasopoulos, G., Di Fonzo, T., and Hyndman, R. J. (2023). Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues. International Journal of Forecasting.
This package is free and open source software, licensed under GPL-3.