This package is considered a duplicate. The official version of this package is found at:https://smac-group.r-universe.dev/simts

Package: simts 0.2.2

Stéphane Guerrier

simts: Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi:10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Authors:Stéphane Guerrier [aut, cre, cph], James Balamuta [aut, cph], Roberto Molinari [aut, cph], Justin Lee [aut], Lionel Voirol [aut], Yuming Zhang [aut], Wenchao Yang [ctb], Nathanael Claussen [ctb], Yunxiang Zhang [ctb], Christian Gunning [cph], Romain Francois [cph], Ross Ihaka [cph], R Core Team [cph]

simts_0.2.2.tar.gz
simts_0.2.2.tar.gz(r-4.5-noble)simts_0.2.2.tar.gz(r-4.4-noble)
simts_0.2.2.tgz(r-4.4-emscripten)simts_0.2.2.tgz(r-4.3-emscripten)
simts.pdf |simts.html✨
simts/json (API)
NEWS

# Install 'simts' in R:

install.packages('simts', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/smac-group/simts/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

australia - Quarterly Increase in Stocks Non-Farm Total, Australia
hydro - Mean Monthly Precipitation, from 1907 to 1972
savingrt - Personal Saving Rate

openblas cpp

4.55 score 4 packages 59 scripts 270 downloads 1 mentions 91 exports 33 dependencies

Last updated 1 years agofrom:c5641f497a. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 23 2024
R-4.5-linux-x86_64	NOTE	Dec 23 2024

Exports:AR AR1 ar1_to_gm ARIMA ARMA ARMA11 auto_corr best_model check comb compare_acf conv.ar1.to.gm conv.gm.to.ar1 corr_analysis create_imu desc.to.ts.model diag_boxpierce diag_ljungbox DR estimate evaluate FGN gen_ar1 gen_ar1blocks gen_arima gen_arma gen_arma11 gen_bi gen_dr gen_fgn gen_generic_sarima gen_gts gen_lts gen_lts_cpp gen_ma1 gen_matern gen_mean gen_model gen_nswn gen_powerlaw gen_qn gen_rw gen_sarima gen_sarma gen_sin gen_wn GM gm_to_ar1 gmwm gmwm_master_cpp gts gts_time has imu is.gts is.imu is.lts is.ts.model is.whole lts M MA Ma_cpp Ma_cpp_vec MA1 make_frame MAPE MAT model_objdesc model_process_desc model_theta np_boot_sd_med orderModel PLP QN RW RW2dimension SARIMA SARMA select select_ar select_arima select_arma select_ma SIN theo_acf theo_pacf unitConversion update_obj value WN

Dependencies:backports broom cli colorspace cpp11 dplyr fansi farver generics glue labeling lifecycle magrittr munsell pillar pkgconfig purrr R6 RColorBrewer Rcpp RcppArmadillo rlang robcor scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

simts Vignettes

Rendered fromvignettes.Rmdusingknitr::rmarkdownon Dec 23 2024.

Last update: 2022-09-03
Started: 2019-07-19

Help page	Topics
Subset an IMU Object	[.imu
Akaike's Information Criterion	AIC.fitsimts
Create an Autoregressive P [AR(P)] Process	AR
Definition of an Autoregressive Process of Order 1	AR1
AR(1) process to WV	ar1_to_wv
Create an Autoregressive Integrated Moving Average (ARIMA) Process	ARIMA
Create an Autoregressive Moving Average (ARMA) Process	ARMA
ARMA process to WV	arma_to_wv
Definition of an ARMA(1,1)	ARMA11
ARMA(1,1) to WV	arma11_to_wv
Quarterly Increase in Stocks Non-Farm Total, Australia	australia
Empirical ACF and PACF	auto_corr
Select the Best Model	best_model
Diagnostics on Fitted Time Series Model	check
Comparison of Classical and Robust Correlation Analysis Functions	compare_acf
Correlation Analysis Functions	corr_analysis
Analytic second derivative matrix for AR(1) process	deriv_2nd_ar1
Analytic D matrix for ARMA(1,1) process	deriv_2nd_arma11
Analytic second derivative matrix for drift process	deriv_2nd_dr
Analytic second derivative for MA(1) process	deriv_2nd_ma1
Analytic D matrix for AR(1) process	deriv_ar1
Analytic D matrix for ARMA(1,1) process	deriv_arma11
Analytic D matrix for Drift (DR) Process	deriv_dr
Analytic D matrix for MA(1) process	deriv_ma1
Analytic D matrix for Quantization Noise (QN) Process	deriv_qn
Analytic D matrix Random Walk (RW) Process	deriv_rw
Analytic D Matrix for a Gaussian White Noise (WN) Process	deriv_wn
Analytic D matrix of Processes	derivative_first_matrix
Box-Pierce	diag_boxpierce
Ljung-Box	diag_ljungbox
Diagnostic Plot of Residuals	diag_plot
Portmanteau Tests	diag_portmanteau_
Create an Drift (DR) Process	DR
Drift to WV	dr_to_wv
Fit a Time Series Model to Data	estimate
Evalute a time series or a list of time series models	evaluate
Definition of a Fractional Gaussian Noise (FGN) Process	FGN
Generate AR(1) Block Process	gen_ar1blocks
Generate Bias-Instability Process	gen_bi
Simulate a simts TS object using a theoretical model	gen_gts
Generate a Latent Time Series Object Based on a Model	gen_lts
Generate Non-Stationary White Noise Process	gen_nswn
Create a Gauss-Markov (GM) Process	GM
Generalized Method of Wavelet Moments (GMWM)	gmwm
GMWM for (Robust) Inertial Measurement Units (IMUs)	gmwm_imu
Create a simts TS object using time series data	gts
Mean Monthly Precipitation, from 1907 to 1972	hydro
Create an IMU Object	imu
Pulls the IMU time from the IMU object	imu_time
Is simts Object	is.gts is.imu is.lts is.ts.model
Generate a Latent Time Series Object from Data	lts
Definition of a Mean deterministic vector returned by the matrix by vector product of matrix X and vector beta	M
Create an Moving Average Q [MA(Q)] Process	MA
Definition of an Moving Average Process of Order 1	MA1
Moving Average Order 1 (MA(1)) to WV	ma1_to_wv
Default utility function for various plots titles	make_frame
Median Absolute Prediction Error	MAPE
Definition of a Matérn Process	MAT
Bootstrap standard error for the median	np_boot_sd_med
Plot Time Series Forecast Function	plot_pred
Plot the GMWM with the Wavelet Variance	plot.gmwm
Plot Partial Auto-Covariance and Correlation Functions	plot.PACF
Plot Auto-Covariance and Correlation Functions	plot.simtsACF
Definition of a Power Law Process	PLP
Time Series Prediction	predict.fitsimts
Predict future points in the time series using the solution of the Generalized Method of Wavelet Moments	predict.gmwm
Create an Quantisation Noise (QN) Process	QN
Quantisation Noise (QN) to WV	qn_to_wv
Read an IMU Binary File into R	read.imu
Plot the Distribution of (Standardized) Residuals	resid_plot
GMWM for Robust/Classical Comparison	rgmwm
Truncated Normal Distribution Sampling Algorithm	rtruncated_normal
Create an Random Walk (RW) Process	RW
Random Walk to WV	rw_to_wv
Function to Compute Direction Random Walk Moves	RW2dimension
Create a Seasonal Autoregressive Integrated Moving Average (SARIMA) Process	SARIMA
Create a Seasonal Autoregressive Moving Average (SARMA) Process	SARMA
Personal Saving Rate	savingrt
Time Series Model Selection	select
Run Model Selection Criteria on ARIMA Models	select_ar select_arima select_arma select_ma
Basic Diagnostic Plot of Residuals	simple_diag_plot
Simplify and print SARIMA model	simplified_print_SARIMA
Definition of a Sinusoidal (SIN) Process	SIN
Summary of fitsimts object	summary.fitsimts
Summary of GMWM object	summary.gmwm
Theoretical Autocorrelation (ACF) of an ARMA process	theo_acf
Theoretical Partial Autocorrelation (PACF) of an ARMA process	theo_pacf
Update (Robust) GMWM object for IMU or SSM	update.gmwm
Update Object Attribute	update.gts update.imu update.lts
Obtain the value of an object's properties	value value.imu
Create an White Noise (WN) Process	WN
Gaussian White Noise to WV	wn_to_wv

Package: simts 0.2.2

simts: Time Series Analysis Tools

simts Vignettes

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Help Manual

Usage by other packages (reverse dependencies)