Smoothing Long-Memory Time Series

The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) .

Multivariate Time Series Data Imputation

This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.

Manipulate Time Series of Climate Reconstructions

Methods to easily extract and manipulate climate reconstructions for ecological and anthropological analyses, as described in Leonardi et al. (2023) . The package includes datasets of palaeoclimate reconstructions, present observations, and future projections from multiple climate models.

Inferring Causal Effects using Bayesian Structural Time-Series Models

Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) . See the package documentation on GitHub < https://google.github.io/CausalImpact/> to get started.

Dimension Reduction Methods for Multivariate Time Series

Estimates VAR and VARX models with Structured Penalties.

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Analyze and model heteroskedastic behavior in financial time series.

Time Value of Money, Time Series Analysis and Computational Finance

Package for time value of money calculation, time series analysis and computational finance.

Time Series Goodness of Fit and Forecast Evaluation Tests

Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.

Time Series Regression

Time series regression. The dyn class interfaces ts, irts(), zoo() and zooreg() time series classes to lm(), glm(), loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values.

Time Series Analysis

Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.