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actfts: Autocorrelation Tools Featured for Time Series

Lifecycle: experimental

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

Example

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Autocorrelation analysis of time series

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