S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)

An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.

eXtensible Time Series

Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.

Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Time Series Analysis and Computational Finance

Time series analysis and computational finance.

Financial Time Series Objects (Rmetrics)

'S4' classes and various tools for financial time series: Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions.

Time Series Missing Value Imputation

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) .

Interface to 'Dygraphs' Interactive Time Series Charting Library

An R interface to the 'dygraphs' JavaScript charting library (a copy of which is included in the package). Provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.

Unit Root and Cointegration Tests for Time Series Data

Unit root and cointegration tests encountered in applied econometric analysis are implemented.

A Tool Kit for Working with Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

Time Series Database Interface

Provides a common interface to time series databases. The objective is to define a standard interface so users can retrieve time series data from various sources with a simple, common, set of commands, and so programs can be written to be portable with respect to the data source. The SQL implementations also provide a database table design, so users needing to set up a time series database have a reasonably complete way to do this easily. The interface provides for a variety of options with respect to the representation of time series in R. The interface, and the SQL implementations, also handle vintages of time series data (sometime called editions or real-time data). There is also a (not yet well tested) mechanism to handle multilingual data documentation. Comprehensive examples of all the 'TS*' packages is provided in the vignette Guide.pdf with the 'TSdata' package.