Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Analyze and model heteroskedastic behavior in financial time series.

Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, ). For methodology behind the NHMMs, see Helske (2025, ).

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.

Time Series Plot

A fast and elegant time series visualization package. In addition to the standard R plot types, this package supports candle sticks, open-high-low-close, and volume plots. Useful for visualizing any time series data, e.g., stock prices and technical indicators.

'data.table' Time-Series

High-frequency time-series support via 'nanotime' and 'data.table'.

Time Series Representations

Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

Time Series Performance

A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.