Single Differencing to Make Time Series Stationary
Source:R/utils-singlediff-stationary.R
util_singlediff_ts.RdThis function attempts to make a non-stationary time series stationary by applying single differencing. It iteratively increases the differencing order until stationarity is achieved.
Value
If the time series is already stationary or the single differencing is successful, it returns a list as described in the details section. If additional differencing is required, it informs the user and returns a list with ret set to FALSE.
Details
The function calculates the frequency of the input time series using the stats::frequency function.
It then applies single differencing 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 single differencing 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 differencing.
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" in this case.
ret: TRUE to indicate a successful transformation.
If the data requires more single differencing than its frequency allows, it informs the user and returns a list with ret set to FALSE, indicating that double differencing may be needed.
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_log_ts()
Examples
# Example 1: Using a time series dataset
util_singlediff_ts(AirPassengers)
#> Differencing of order 1 made the time series stationary
#> $stationary_ts
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
#> 1949 6 14 -3 -8 14 13 0 -12 -17 -15 14
#> 1950 -3 11 15 -6 -10 24 21 0 -12 -25 -19 26
#> 1951 5 5 28 -15 9 6 21 0 -15 -22 -16 20
#> 1952 5 9 13 -12 2 35 12 12 -33 -18 -19 22
#> 1953 2 0 40 -1 -6 14 21 8 -35 -26 -31 21
#> 1954 3 -16 47 -8 7 30 38 -9 -34 -30 -26 26
#> 1955 13 -9 34 2 1 45 49 -17 -35 -38 -37 41
#> 1956 6 -7 40 -4 5 56 39 -8 -50 -49 -35 35
#> 1957 9 -14 55 -8 7 67 43 2 -63 -57 -42 31
#> 1958 4 -22 44 -14 15 72 56 14 -101 -45 -49 27
#> 1959 23 -18 64 -10 24 52 76 11 -96 -56 -45 43
#> 1960 12 -26 28 42 11 63 87 -16 -98 -47 -71 42
#>
#> $ndiffs
#> [1] 1
#>
#> $adf_stats
#> $adf_stats$test_stat
#> [1] -7.017671
#>
#> $adf_stats$p_value
#> [1] 0.01
#>
#>
#> $trans_type
#> [1] "diff"
#>
#> $ret
#> [1] TRUE
#>
# Example 2: Using a different time series dataset
util_singlediff_ts(BJsales)$ret
#> Data requires more single differencing than its frequency, trying double
#> differencing
#> [1] FALSE