Bayesian Structural Time Series

Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) , among many other sources.

Time Series Feature Extraction

Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) , Kang, Hyndman and Smith-Miles (2017) and from Fulcher, Little and Jones (2013) . Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

Applied Statistical Time Series Analysis

Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, , and Time Series: A Data Analysis Approach Using R (2nd ed). Chapman-Hall, 2026, < https://www.routledge.com/Time-Series-A-Data-Analysis-Approach-Using-R/Shumway-Stoffer/p/book/9781041031642>. Most scripts are designed to require minimal input to produce aesthetically pleasing output for ease of use in live demonstrations and course work.

Functional Time Series Analysis

Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

The Tidymodels Extension for Time Series Modeling

The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (< https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (< https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

Archaeological Time Series

A toolkit for archaeological time series and time intervals. This package provides a system of classes and methods to represent and work with archaeological time series and time intervals. Dates are represented as "rata die" and can be converted to (virtually) any calendar defined by Reingold and Dershowitz (2018) . This packages offers a simple API that can be used by other specialized packages.

Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

Time Series Modeling for Air Pollution and Health

Tools for specifying time series regression models.

Distance Measures for Time Series Data

A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. U. Mori, A. Mendiburu and J.A. Lozano (2016), .

Locally Stationary Time Series

A set of functions that allow stationary analysis and locally stationary time series analysis.