This function attempts to make a non-stationary time series stationary by applying double differencing with a logarithmic transformation. It iteratively increases the differencing order until stationarity is achieved or informs the user if the transformation is not possible.
util_doubledifflog_ts(.time_series)If the time series is already stationary or the double differencing with a logarithmic transformation is successful, it returns a list as described in the details section. If the transformation is not possible, it informs the user and returns a list with ret set to FALSE, indicating that the data could not be stationarized.
A time series object to be made stationary.
Steven P. Sanderson II, MPH
The function calculates the frequency of the input time series using the stats::frequency function
and checks if the minimum value of the time series is greater than 0. It then applies double differencing
with a logarithmic transformation incrementally until the Augmented Dickey-Fuller test indicates
stationarity (p-value < 0.05) or until the differencing order reaches the frequency of the data.
If double differencing with a logarithmic transformation successfully makes the time series stationary, it returns the stationary time series and related information as a list with the following elements:
stationary_ts: The stationary time series after the transformation.
ndiffs: The order of differencing applied to make it stationary.
adf_stats: Augmented Dickey-Fuller test statistics on the stationary time series.
trans_type: Transformation type, which is "double_diff_log" in this case.
ret: TRUE to indicate a successful transformation.
If the data either had a minimum value less than or equal to 0 or requires more differencing than its frequency allows, it informs the user that the data could not be stationarized.
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_log_ts(),
util_singlediff_ts()
# Example 1: Using a time series dataset
util_doubledifflog_ts(AirPassengers)
# Example 2: Using a different time series dataset
util_doubledifflog_ts(BJsales)$ret
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