R-universe search: needs:fabletools

Showing 19 of total 19 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.

forecasting cpp

569 stars 13.54 score 2.1k scripts 6 dependents

tidyverts

feasts:Feature Extraction and Statistics for Time Series

Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.

Maintained by Mitchell OHara-Wild. Last updated 5 months ago.

300 stars 12.38 score 1.4k scripts 7 dependents

microsoft

finnts:Microsoft Finance Time Series Forecasting Framework

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Maintained by Mike Tokic. Last updated 1 months ago.

business data-science feature-selection finance finnts forecasting machine-learning microsoft time-series

194 stars 9.30 score 39 scripts

njtierney

brolgar:Browse Over Longitudinal Data Graphically and Analytically in R

Provides a framework of tools to summarise, visualise, and explore longitudinal data. It builds upon the tidy time series data frames used in the 'tsibble' package, and is designed to integrate within the 'tidyverse', and 'tidyverts' (for time series) ecosystems. The methods implemented include calculating features for understanding longitudinal data, including calculating summary statistics such as quantiles, medians, and numeric ranges, sampling individual series, identifying individual series representative of a group, and extending the facet system in 'ggplot2' to facilitate exploration of samples of data. These methods are fully described in the paper "brolgar: An R package to Browse Over Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

Maintained by Nicholas Tierney. Last updated 3 months ago.

109 stars 8.73 score 141 scripts

robjhyndman

fpp3:Data for "Forecasting: Principles and Practice" (3rd Edition)

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.

Maintained by Rob Hyndman. Last updated 7 months ago.

data forecasting

142 stars 8.54 score 2.5k scripts

hendersontrent

theft:Tools for Handling Extraction of Features from Time Series

Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

Maintained by Trent Henderson. Last updated 2 months ago.

data-visualisation data-visualization dimensionality-reduction machine-learning time-series

40 stars 7.48 score 50 scripts 1 dependents

robjhyndman

vital:Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data

Analysing vital statistics based on tools consistent with the tidyverse. Tools are provided for data visualization, life table calculations, computing net migration numbers, Lee-Carter modelling; functional data modelling and forecasting.

Maintained by Rob Hyndman. Last updated 5 days ago.

28 stars 7.20 score 18 scripts

ykang

gratis:Generating Time Series with Diverse and Controllable Characteristics

Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.

Maintained by Feng Li. Last updated 12 months ago.

data-generation mixture-autoregressive statistical-computing time-series

76 stars 6.98 score 25 scripts

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.

56 stars 6.48 score 107 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 29 days ago.

echo-state-networks fable fabletools forecast forecasting recurrent-neural-networks reservoir-computing ridge-regression time-series openblas cpp openmp

12 stars 6.03 score 8 scripts

epe-gov-br

epe4md:EPE's 4MD model to forecast the adoption of Distributed Generation and Behind-the-meter energy storage

EPE's 4MD model to forecast the adoption of Distributed Generation and Behind-the-meter energy storage

Maintained by Gabriel Konzen. Last updated 21 days ago.

19 stars 5.58 score 5 scripts

hendersontrent

theftdlc:Analyse and Interpret Time Series Features

Provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the 'theft' package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <arXiv:2303.17809>.

Maintained by Trent Henderson. Last updated 2 months ago.

data-science data-visualization machine-learning statistics time-series

4 stars 4.94 score 11 scripts

sevvandi

oddnet:Anomaly Detection in Temporal Networks

Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.

Maintained by Sevvandi Kandanaarachchi. Last updated 10 months ago.

3 stars 4.22 score 11 scripts

inzightvit

iNZightTS:Time Series for 'iNZight'

Provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".

Maintained by Tom Elliott. Last updated 11 months ago.

1 stars 3.48 score 5 scripts

sevvandi

netseer:Graph Prediction from a Graph Time Series

Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2024) <doi:10.48550/arXiv.2401.04280>.

Maintained by Sevvandi Kandanaarachchi. Last updated 5 months ago.

3.40 score 2 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.

ataforecasting fable fabletools forecast forecasting

4 stars 3.30 score 9 scripts

cran

ForecastingEnsembles:Time Series Forecasting Using 23 Individual Models

Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data. For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon.

Maintained by Russ Conte. Last updated 10 hours ago.

2.70 score

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

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.

1 stars 1.00 score