Class-Agnostic Time Series

Time series toolkit with identical behavior for all time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo', 'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.

Functions for Time Series Analysis

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.

Multivariate Time Series Plot

A function for plotting multivariate time series data.

Time Series Costationarity Determination

Contains functions that can determine whether a time series is second-order stationary or not (and hence evidence for locally stationarity). Given two non-stationary series (i.e. locally stationary series) this package can then discover time-varying linear combinations that are second-order stationary. Cardinali, A. and Nason, G.P. (2013) .

Time Series Analysis 'OpenBudgets.eu'

Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' < http://openbudgets.eu/> time series data. Calculate time series model and forecast parameters in budget time series data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring deterministic and stochastic trend of the input time series data with 'ACF', 'PACF', 'Phillips Perron' test, 'Augmented Dickey Fuller (ADF)' test, 'Kwiatkowski-Phillips-Schmidt-Shin (KPSS)' test, 'Mann Kendall' test for monotonic trend and 'Cox and Stuart' trend test, decomposing with local regression models or 'stl' decomposition, fitting the appropriate 'arima' model and provide forecasts for the input 'OpenBudgets.eu' time series fiscal data. Also, can be used generally to extract visualization parameters convert them to 'JSON' format and use them as input in a different graphical interface.

From Time Series to Networks

Transforming one or multiple time series into networks. This package is useful for complex systems modeling, time series data mining, or time series analysis using networks. An introduction to the topic and the descriptions of the methods implemented in this package can be found in Mitchell (2006) , Silva and Zhao (2016) , and Silva et al. (2021) .

Time Series Data Sets

Provides a diverse collection of time series datasets spanning various fields such as economics, finance, energy, healthcare, and more. Designed to support time series analysis in R by offering datasets from multiple disciplines, making it a valuable resource for researchers and analysts.

Brazilian Economic Time Series

It provides access to and information about the most important Brazilian economic time series - from the Getulio Vargas Foundation < http://portal.fgv.br/en>, the Central Bank of Brazil < http://www.bcb.gov.br> and the Brazilian Institute of Geography and Statistics < http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g. generating dynamic reports with a complete analysis of a series) and exporting these time series.

Ordinal Time Series Analysis

An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) can be computed. These features can be used to perform inferential tasks or to feed machine learning algorithms for ordinal time series, among others. The package also includes some interesting datasets containing financial time series. Practitioners from a broad variety of fields could benefit from the general framework provided by 'otsfeatures'.

Positive Time Series Regression

A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. This package implements the results presented in Prass, T.S.; Carlos, J.H.; Taufemback, C.G. and Pumi, G. (2022). "Positive Time Series Regression" .