Learn R Programming

⚠️There's a newer version (1.1.3) of this package.Take me there.

NTS (version 1.1.2)

Nonlinear Time Series Analysis

Description

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Copy Link

Version

Install

install.packages('NTS')

Monthly Downloads

301

Version

1.1.2

License

GPL (>= 2)

Maintainer

Xialu Liu

Last Published

August 6th, 2020

Functions in NTS (1.1.2)

F Test for a CFAR Process with Heteroscedasticity and Irregular Observation Locations

Estimation of Autoregressive Conditional Mean Models

Setting Up The Predictor Matrix in A Neural Network for Time Series Data

F Test for a CFAR Process

F Test for Nonlinearity

Generate Univariate 2-regime Markov Switching Models

Full Information Propagation Step under Mixture Kalman Filter

Fitting Univariate Autoregressive Markov Switching Models

One Propagation Step under Mixture Kalman Filter for Fading Channels

Tsay Test for Nonlinearity

Backtest for Univariate TAR Models

Generate a CFAR(2) Process

Generic Sequential Monte Carlo Using Full Information Proposal Distribution and Rao-Blackwellization

Sequential Importance Sampling Step for Fading Channels

Generate a CFAR(1) Process

General Estimation of TAR Models

Prediction of A Fitted Univariate TAR Model

Sequential Importance Sampling Step for A Target with Passive Sonar

Kalman Filter for Tracking in Clutter

Sstep.Smooth.Sonar

Sequential Importance Sampling for A Target with Passive Sonar

Sstep.Clutter.Full

Sequential Importance Sampling under Clutter Environment

Estimation of Multivariate TAR Models

Estimation of a Multivariate Two-Regime SETAR Model

simuTargetClutter

Simulate A Moving Target in Clutter

Sequential Monte Carlo for A Moving Target under Clutter Environment

Simulate Signals from A System with Rayleigh Flat-Fading Channels

Generic Sequential Monte Carlo Using Full Information Proposal Distribution

Estimation of a CFAR Process

Check linear models with cross validation

Generic Sequential Monte Carlo Method

Generate a CFAR(2) Process with Heteroscedasticity and Irregular Observation Locations

Generate a CFAR Process

Estimation of a CFAR Process with Heteroscedasticity and Irregualar Observation Locations

Rank-Based Portmanteau Tests

Generic Sequential Monte Carlo Smoothing with Marginal Weights

Prediction of A Fitted Multivariate TAR Model

Filtering and Smoothing for Time-Varying AR Models

Estimating of Random-Coefficient AR Models

Estimation of a Univariate Two-Regime SETAR Model

Generate Two-Regime (TAR) Models

Refine A Fitted 2-Regime Multivariate TAR Model

Generate Univariate SETAR Models

Create Dummy Variables for High-Frequency Intraday Seasonality

Sequential Monte Carlo Using Sequential Importance Sampling for Stochastic Volatility Models

simPassiveSonar

Simulate A Sample Trajectory

Prediction of CFAR Processes

Estimate Time-Varying Coefficient AR Models

Sstep.Clutter.Full.RB

Sequential Importance Sampling under Clutter Environment

Threshold Nonlinearity Test

Partial Curve Prediction of CFAR Processes