Logarithmic Transformation to Make Time Series Stationary
Source:R/utils-log-trans-stationary.R
util_log_ts.RdThis function attempts to make a non-stationary time series stationary by applying a logarithmic transformation. If successful, it returns the stationary time series. If the transformation fails, it informs the user.
Value
If the time series is already stationary or the logarithmic transformation is successful, it returns a list as described in the details section. If the transformation fails, it returns a list with ret set to FALSE.
Details
This function checks if the minimum value of the input time series is greater than or equal to zero. If yes, it performs the Augmented Dickey-Fuller test on the logarithm of the time series. If the p-value of the test is less than 0.05, it concludes that the logarithmic transformation made the time series stationary and returns the result as a list with the following elements:
stationary_ts: The stationary time series after the logarithmic transformation.
ndiffs: Not applicable in this case, marked as NA.
adf_stats: Augmented Dickey-Fuller test statistics on the stationary time series.
trans_type: Transformation type, which is "log" in this case.
ret: TRUE to indicate a successful transformation.
If the minimum value of the time series is less than or equal to 0 or if the logarithmic transformation doesn't make the time series stationary, it informs the user and returns a list with ret set to FALSE.
See also
Other Utility:
auto_stationarize(),
calibrate_and_plot(),
internal_ts_backward_event_tbl(),
internal_ts_both_event_tbl(),
internal_ts_forward_event_tbl(),
model_extraction_helper(),
ts_get_date_columns(),
ts_info_tbl(),
ts_is_date_class(),
ts_lag_correlation(),
ts_model_auto_tune(),
ts_model_compare(),
ts_model_rank_tbl(),
ts_model_spec_tune_template(),
ts_qq_plot(),
ts_scedacity_scatter_plot(),
ts_to_tbl(),
util_difflog_ts(),
util_doublediff_ts(),
util_doubledifflog_ts(),
util_singlediff_ts()
Examples
# Example 1: Using a time series dataset
util_log_ts(AirPassengers)
#> Logrithmic transformation made the time series stationary
#> $stationary_ts
#> Jan Feb Mar Apr May Jun Jul Aug
#> 1949 4.718499 4.770685 4.882802 4.859812 4.795791 4.905275 4.997212 4.997212
#> 1950 4.744932 4.836282 4.948760 4.905275 4.828314 5.003946 5.135798 5.135798
#> 1951 4.976734 5.010635 5.181784 5.093750 5.147494 5.181784 5.293305 5.293305
#> 1952 5.141664 5.192957 5.262690 5.198497 5.209486 5.384495 5.438079 5.488938
#> 1953 5.278115 5.278115 5.463832 5.459586 5.433722 5.493061 5.575949 5.605802
#> 1954 5.318120 5.236442 5.459586 5.424950 5.455321 5.575949 5.710427 5.680173
#> 1955 5.488938 5.451038 5.587249 5.594711 5.598422 5.752573 5.897154 5.849325
#> 1956 5.648974 5.624018 5.758902 5.746203 5.762051 5.924256 6.023448 6.003887
#> 1957 5.752573 5.707110 5.874931 5.852202 5.872118 6.045005 6.142037 6.146329
#> 1958 5.828946 5.762051 5.891644 5.852202 5.894403 6.075346 6.196444 6.224558
#> 1959 5.886104 5.834811 6.006353 5.981414 6.040255 6.156979 6.306275 6.326149
#> 1960 6.033086 5.968708 6.037871 6.133398 6.156979 6.282267 6.432940 6.406880
#> Sep Oct Nov Dec
#> 1949 4.912655 4.779123 4.644391 4.770685
#> 1950 5.062595 4.890349 4.736198 4.941642
#> 1951 5.214936 5.087596 4.983607 5.111988
#> 1952 5.342334 5.252273 5.147494 5.267858
#> 1953 5.468060 5.351858 5.192957 5.303305
#> 1954 5.556828 5.433722 5.313206 5.433722
#> 1955 5.743003 5.613128 5.468060 5.627621
#> 1956 5.872118 5.723585 5.602119 5.723585
#> 1957 6.001415 5.849325 5.720312 5.817111
#> 1958 6.001415 5.883322 5.736572 5.820083
#> 1959 6.137727 6.008813 5.891644 6.003887
#> 1960 6.230481 6.133398 5.966147 6.068426
#>
#> $ndiffs
#> [1] NA
#>
#> $adf_stats
#> $adf_stats$test_stat
#> [1] -6.421458
#>
#> $adf_stats$p_value
#> [1] 0.01
#>
#>
#> $trans_type
#> [1] "log"
#>
#> $ret
#> [1] TRUE
#>
# Example 2: Using a different time series dataset
util_log_ts(BJsales.lead)$ret
#> Logrithmic Transformation Failed.
#> [1] FALSE