Detection of Outliers in Time Series

Detection of outliers in time series following the Chen and Liu (1993) procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

Hierarchical and Grouped Time Series

Provides methods for analysing and forecasting hierarchical and grouped time series. The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) , and trace minimization reconciliation (Wickramasuriya et al. 2018) .

All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Companion to Tsay (2005) Analysis of Financial Time Series

R companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.

Package for Analysis of Space-Time Ecological Series

Regularisation, decomposition and analysis of space-time series. The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff ) initiative to bring PASSTEC 2000 functionalities to R.

Unit Root Tests for Seasonal Time Series

Seasonal unit roots and seasonal stability tests. P-values based on response surface regressions are available for both tests. P-values based on bootstrap are available for seasonal unit root tests.

'NetCDF' Geometry and Time Series

Tools to create time series and geometry 'NetCDF' files.

Nonlinear Time Series Analysis

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.

Bayesian Structural Time Series

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

Interpreting Time Series and Autocorrelated Data Using GAMMs

GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).