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) , Winters (1960) , Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".

Interface to 'JDemetra+' 3.x Time Series Analysis Software

Interface to 'JDemetra+' 3.x (< https://github.com/jdemetra>) time series analysis software. It offers full access to txt, csv, xml and spreadsheets files which are meant to be read by 'JDemetra+' Graphical User Interface.

Fractional ARIMA (and Other Long Memory) Time Series Modeling

Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at < https://github.com/JQVeenstra/arfima>), where the development version of this package lives; it can be installed using devtools.

Evaluation Metrics for Machine Learning

An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

Analysis of Feedback in Time Series

Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.

Time Series for Data Science

Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.

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 Clustering Utilities

A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.

Point Process Time Series

Provides functions for point process time series. Autocorrelation functions for spatial and temporal time series, and estimation of trend-plus-seasonality models for temporal and spatial time series. See Gervini (2025) and Gervini and Kopischke (2026) .

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.