Showing 9 of total 9 results (show query)
tidyverts
fable:Forecasting Models for Tidy Time Series
Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.
Maintained by Mitchell OHara-Wild. Last updated 4 months ago.
156.8 match 565 stars 13.52 score 2.1k scripts 6 dependents
mitchelloharawild
fable.prophet:Prophet Modelling Interface for 'fable'
Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.
Maintained by Mitchell OHara-Wild. Last updated 1 years ago.
46.8 match 56 stars 6.48 score 107 scriptstidyverts
fabletools:Core Tools for Packages in the 'fable' Framework
Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Maintained by Mitchell OHara-Wild. Last updated 1 months ago.
14.5 match 91 stars 12.18 score 396 scripts 18 dependents
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
52.3 match 4 stars 3.30 score 9 scripts
ahaeusser
echos:Echo State Networks for Time Series Modeling and Forecasting
Provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).
Maintained by Alexander Häußer. Last updated 13 days ago.
echo-state-networksfablefabletoolsforecastforecastingrecurrent-neural-networksreservoir-computingridge-regressiontime-seriesopenblascppopenmp
10.0 match 12 stars 6.03 score 8 scriptsalsabtay
ATAforecasting:Automatic Time Series Analysis and Forecasting using the Ata Method
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). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Maintained by Ali Sabri Taylan. Last updated 2 years ago.
ataataforecastingfableforecastforecastingtime-seriescpp
10.0 match 5 stars 3.88 score 4 scripts 1 dependents
cran
fableCount:INGARCH and GLARMA Models for Count Time Series in Fable Framework
Provides a tidy R interface for count time series analysis. It includes implementation of the INGARCH (Integer Generalized Autoregressive Conditional Heteroskedasticity) model from the 'tscount' package and the GLARMA (Generalized Linear Autoregressive Moving Averages) model from the 'glarma' package. Additionally, it offers automated parameter selection algorithms based on the minimization of a penalized likelihood.
Maintained by Gustavo Almeida. Last updated 12 months ago.
17.9 match 1 stars 1.00 score
pik-piam
mrfable:FABLE project data
Tool for easy downloading, cleaning, and sorting foodcrop data for India taken from here: https://eands.dacnet.nic.in/APY_96_To_07.htm .
Maintained by Anastasis Giannousakis. Last updated 1 years ago.
3.3 match 2 stars 3.60 score 3 scripts
epiforecasts
EpiSoon:Forecast Cases Using Reproduction Numbers
To forecast the time-varying reproduction number and use this to forecast reported case counts. Includes tools to evaluate a range of models across samples and time series using proper scoring rules.
Maintained by Sam Abbott. Last updated 2 years ago.
2.0 match 7 stars 4.26 score 25 scripts 1 dependents