Description
Autocorrelation Tools Featured for Time Series.
Description
The 'actfts' package provides tools for performing autocorrelation analysis of time series data. It includes functions to compute and visualize the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Additionally, it performs the Dickey-Fuller, KPSS, and Phillips-Perron unit root tests to assess the stationarity of time series. Theoretical foundations are based on Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Box and Jenkins (1976) <isbn:978-0-8162-1234-2>, and Box and Pierce (1970) <doi:10.1080/01621459.1970.10481180>. Statistical methods are also drawn from Kolmogorov (1933) <doi:10.1007/BF00993594>, Kwiatkowski et al. (1992) <doi:10.1016/0304-4076(92)90104-Y>, and Ljung and Box (1978) <doi:10.1093/biomet/65.2.297>. The package integrates functions from 'forecast' (Hyndman & Khandakar, 2008) <https://CRAN.R-project.org/package=forecast>, 'tseries' (Trapletti & Hornik, 2020) <https://CRAN.R-project.org/package=tseries>, 'xts' (Ryan & Ulrich, 2020) <https://CRAN.R-project.org/package=xts>, and 'stats' (R Core Team, 2023) <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/00Index.html>. Additionally, it provides visualization tools via 'plotly' (Sievert, 2020) <https://CRAN.R-project.org/package=plotly> and 'reactable' (Glaz, 2023) <https://CRAN.R-project.org/package=reactable>. The package also incorporates macroeconomic datasets from the U.S. Bureau of Economic Analysis: Disposable Personal Income (DPI) <https://fred.stlouisfed.org/series/DPI>, Gross Domestic Product (GDP) <https://fred.stlouisfed.org/series/GDP>, and Personal Consumption Expenditures (PCEC) <https://fred.stlouisfed.org/series/PCEC>.
README.md
actfts: Autocorrelation Tools Featured for Time Series
The actfts package offers a flexible approach to time series analysis by focusing on Autocorrelation (ACF), Partial Autocorrelation (PACF), and stationarity tests, generating interactive plots for dynamic data visualization. It processes input data by validating and transforming it according to specified differences. It calculates ACF and PACF up to several lags and performs Box-Pierce, Ljung-Box, ADF, KPSS, and PP tests. The function organizes results into tables, with options to save them as TIFF files or Excel spreadsheets, and interactive mode provides on-screen visualization of the ACF-PACF and stationarity test outcomes.
Installation
You can install the development version of actfts from:
install.packages("actfts")
devtools::install_github("SergioFinances/actfts")
Example
This is a basic example which shows you how to use actfts packcage:
library(actfts)
data <- actfts::GDPEEUU
result <- actfts::acfinter(data, lag = 10)
print(result)
#> $`ACF-PACF Test`
#> lag acf pacf Box_Pierce Pv_Box Ljung_Box Pv_Ljung
#> 1 1 0.9849981 0.9849981360 300.7686 0 303.6887 0
#> 2 2 0.9702311 0.0003276145 592.5866 0 599.2965 0
#> 3 3 0.9555439 -0.0047507255 875.6365 0 886.9564 0
#> 4 4 0.9409487 -0.0043825005 1150.1057 0 1166.8073 0
#> 5 5 0.9267058 0.0043661368 1416.3286 0 1439.1403 0
#> 6 6 0.9125019 -0.0058830766 1674.4531 0 1704.0575 0
#> 7 7 0.8985872 0.0023755846 1924.7654 0 1961.8049 0
#> 8 8 0.8849702 0.0028907399 2167.5489 0 2212.6275 0
#> 9 9 0.8716864 0.0043086528 2403.0984 0 2456.7851 0
#> 10 10 0.8588828 0.0093318494 2631.7791 0 2694.6130 0
#>
#> $`Stationary Test`
#> Statistic P_Value
#> ADF 2.548975 0.99
#> KPSS-Level 4.698172 0.01
#> KPSS-Trend 1.206680 0.01
#> PP 3.713440 0.99
#>
#> $`Normality Test`
#> Statistic P_Value
#> Shapiro Wilks 0.84660 0
#> Kolmogorov Smirnov 0.17612 0
#> Box Cox 0.10000 NA
#> Box Cox Guerrero -0.00772 NA
References
- U.S. Bureau of Economic Analysis, Gross Domestic Product (GDP), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/GDP.
- U.S. Bureau of Economic Analysis, Personal Consumption Expenditures (PCEC), retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCEC.
- U.S. Bureau of Economic Analysis, Disposable Personal Income (DPI), retrieved from FRED, Federal Reserve Bank of St. Louis;https://fred.stlouisfed.org/series/DPI.