util_difflog_ts: Differencing with Log Transformation to Make Time Series...

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

Differencing with Log Transformation to Make Time Series Stationary

Description

This function attempts to make a non-stationary time series stationary by applying 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.

Usage

util_difflog_ts(.time_series)

Arguments

.time_series

A time series object to be made stationary.

Details

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 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 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 "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 and suggests trying double differencing with a logarithmic transformation.

Value

If the time series is already stationary or the differencing with a logarithmic transformation is successful,

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_doublediff_ts(), util_doubledifflog_ts(), util_log_ts(), util_singlediff_ts()

Examples

# Example 1: Using a time series dataset
util_difflog_ts(AirPassengers)

# Example 2: Using a different time series dataset
util_difflog_ts(BJsales)$ret

healthyR.ts documentation built on Nov. 15, 2023, 9:07 a.m.