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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.
You can install the development version of actfts from:
install.packages("actfts")
::install_github("SergioFinances/actfts") devtools
This is a basic example which shows you how to use actfts packcage:
library(actfts)
<- actfts::GDPEEUU
data <- actfts::acfinter(data, lag = 10)
result 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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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