util_log_ts: Logarithmic Transformation to Make Time Series Stationary

In spsanderson/healthyR.ts: The Time Series Modeling Companion to 'healthyR'

Logarithmic Transformation to Make Time Series Stationary

Description

This 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.

Usage

util_log_ts(.time_series)

Arguments

.time_series

A time series object to be made stationary.

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.

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.

Author(s)

Steven P. Sanderson II, MPH

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)

# Example 2: Using a different time series dataset
util_log_ts(BJsales.lead)$ret

spsanderson/healthyR.ts documentation built on Oct. 18, 2024, 5:51 p.m.