util_doubledifflog_ts: Double Differencing with Log Transformation to Make Time...

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

Double Differencing with Log Transformation to Make Time Series Stationary

Description

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.

Usage

util_doubledifflog_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 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.

Value

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.

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_log_ts(), util_singlediff_ts()

Examples

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

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

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