Showing 4 of total 4 results (show query)
marcburri
bridgr:Bridging Data Frequencies for Timely Economic Forecasts
Provides tools for implementing bridge models, which are used to nowcast and forecast macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Forecasting and aggregation of the indicator variables to match the target frequency are simplified, enabling timely predictions before official data releases are available. For more information about bridge models, see Baffigi, A., Golinelli, R., \& Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Burri (2023) <https://www5.unine.ch/RePEc/ftp/irn/pdfs/WP23-02.pdf> or Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>.
Maintained by Marc Burri. Last updated 4 months ago.
2 stars 4.52 score 11 scripts
alsabtay
fable.ata:'ATAforecasting' Modelling Interface for 'fable' Framework
Allows ATA (Automatic Time series analysis using the Ata method) models from the 'ATAforecasting' package to be used in a tidy workflow with the modeling interface of 'fabletools'. This extends 'ATAforecasting' to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).
Maintained by Ali Sabri Taylan. Last updated 2 years ago.
ataforecastingfablefabletoolsforecastforecasting
4 stars 3.30 score 9 scripts
robjhyndman
TACforecasting:Forecasting Functions for the Transport Accident Commission
Functions to make hierarchical time series forecasts of attendant care hours easier.
Maintained by Rob Hyndman. Last updated 2 years ago.
2 stars 2.00 score 3 scripts
danielollech
tssim:Simulation of Daily and Monthly Time Series
Flexible simulation of time series using time series components, including seasonal, calendar and outlier effects. Main algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.
Maintained by Daniel Ollech. Last updated 4 months ago.
1.00 score