Automatically Stationarize Time Series Data
Source:R/utils-auto-stationarize.R
auto_stationarize.RdThis function attempts to make a non-stationary time series stationary. This function attempts to make a given time series stationary by applying transformations such as differencing or logarithmic transformation. If the time series is already stationary, it returns the original time series.
Value
If the time series is already stationary, it returns the original time series. If a transformation is applied to make it stationary, it returns a list with two elements:
stationary_ts: The stationary time series.
ndiffs: The order of differencing applied to make it stationary.
Details
If the input time series is non-stationary (determined by the Augmented Dickey-Fuller test), this function will try to make it stationary by applying a series of transformations:
It checks if the time series is already stationary using the Augmented Dickey-Fuller test.
If not stationary, it attempts a logarithmic transformation.
If the logarithmic transformation doesn't work, it applies differencing.
See also
Other Utility:
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(),
util_singlediff_ts()
Examples
# Example 1: Using the AirPassengers dataset
auto_stationarize(AirPassengers)
#> The time series is already stationary via ts_adf_test().
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
#> 1949 112 118 132 129 121 135 148 148 136 119 104 118
#> 1950 115 126 141 135 125 149 170 170 158 133 114 140
#> 1951 145 150 178 163 172 178 199 199 184 162 146 166
#> 1952 171 180 193 181 183 218 230 242 209 191 172 194
#> 1953 196 196 236 235 229 243 264 272 237 211 180 201
#> 1954 204 188 235 227 234 264 302 293 259 229 203 229
#> 1955 242 233 267 269 270 315 364 347 312 274 237 278
#> 1956 284 277 317 313 318 374 413 405 355 306 271 306
#> 1957 315 301 356 348 355 422 465 467 404 347 305 336
#> 1958 340 318 362 348 363 435 491 505 404 359 310 337
#> 1959 360 342 406 396 420 472 548 559 463 407 362 405
#> 1960 417 391 419 461 472 535 622 606 508 461 390 432
# Example 2: Using the BJsales dataset
auto_stationarize(BJsales)
#> The time series is not stationary. Attempting to make it stationary...
#> Logrithmic Transformation Failed.
#> Data requires more single differencing than its frequency, trying double
#> differencing
#> Double Differencing of order 1 made the time series stationary
#> $stationary_ts
#> Time Series:
#> Start = 3
#> End = 150
#> Frequency = 1
#> [1] 0.5 -0.4 0.6 1.1 -2.8 3.0 -1.1 0.6 -0.5 -0.5 0.1 2.0 -0.6 0.8 1.2
#> [16] -3.4 -0.7 -0.3 1.7 3.0 -3.2 0.9 2.2 -2.5 -0.4 2.6 -4.3 2.0 -3.1 2.7
#> [31] -2.1 0.1 2.1 -0.2 -2.2 0.6 1.0 -2.6 3.0 0.3 0.2 -0.8 1.0 0.0 3.2
#> [46] -2.2 -4.7 1.2 0.8 -0.6 -0.4 0.6 1.0 -1.6 -0.1 3.4 -0.9 -1.7 -0.5 0.8
#> [61] 2.4 -1.9 0.6 -2.2 2.6 -0.1 -2.7 1.7 -0.3 1.9 -2.7 1.1 -0.6 0.9 0.0
#> [76] 1.8 -0.5 -0.4 -1.2 2.6 -1.8 1.7 -0.9 0.6 -0.4 3.0 -2.8 3.1 -2.3 -1.1
#> [91] 2.1 -0.3 -1.7 -0.8 -0.4 1.1 -1.5 0.3 1.4 -2.0 1.3 -0.3 0.4 -3.5 1.1
#> [106] 2.6 0.4 -1.3 2.0 -1.6 0.6 -0.1 -1.4 1.6 1.6 -3.4 1.7 -2.2 2.1 -2.0
#> [121] -0.2 0.2 0.7 -1.4 1.8 -0.1 -0.7 0.4 0.4 1.0 -2.4 1.0 -0.4 0.8 -1.0
#> [136] 1.4 -1.2 1.1 -0.9 0.5 1.9 -0.6 0.3 -1.4 -0.9 -0.5 1.4 0.1
#>
#> $ndiffs
#> [1] 1
#>
#> $adf_stats
#> $adf_stats$test_stat
#> [1] -6.562008
#>
#> $adf_stats$p_value
#> [1] 0.01
#>
#>
#> $trans_type
#> [1] "double_diff"
#>
#> $ret
#> [1] TRUE
#>