Raster Time Series Analysis

This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).

Time Series Outlier Detection

Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.

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.

Interactive Time Series Visualizations

Create interactive time series visualizations. 'linevis' includes an extensive API to manipulate time series after creation, and supports getting data out of the visualization. Based on the 'timevis' package and the 'vis.js' Timeline 'JavaScript' library < https://visjs.github.io/vis-timeline/docs/graph2d/>.