tsibble - Tidy Temporal Data Frames and Tools
Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.
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14.73 score 544 stars 41 dependents 5.4k scripts 23k downloads
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.
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forecastingcpp
13.26 score 586 stars 7 dependents 2.2k scripts 8.6k downloadsfabletools - 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.
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12.41 score 96 stars 24 dependents 581 scripts 11k downloads
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.
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12.25 score 309 stars 9 dependents 1.8k scripts 7.7k downloads
tsibbledata - Diverse Datasets for 'tsibble'
Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.
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datasettsibble
8.28 score 27 stars 2 dependents 826 scripts 5.6k downloads
fasster - Fast Additive Switching of Seasonality, Trend, and Exogenous Regressors
Implementation of the FASSTER (Forecasting with Additive Switching of Seasonality, Trend, and Exogenous Regressors) model for forecasting time series with multiple seasonal patterns. The model combines state space methodology with a switching component in the observation equation to allow flexible modeling of complex seasonal patterns, including time-varying effects and multiple seasonalities.
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7.44 score 153 stars 119 scripts 194 downloads