{healthyR.ts} New Features: Unlocking More Power
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
New Features: Unlocking More Power
My R package {healthyR.ts} has been updated to version 0.3.0; you can install it from either CRAN, r-universe or GitHub. Let’s go over some of the changes and improvements.
News
1. util_log_ts() – Logging Time Series Data
One of the standout additions is the introduction of util_log_ts(). This function seems like a game-changer, providing a streamlined way to log time series data. This is incredibly useful, especially when dealing with extensive datasets, making the whole process more efficient and user-friendly. This is a helper function for auto_stationarize().
2. util_singlediff_ts() – Single Differences for Time Series
The addition of util_singlediff_ts() expands the toolkit, offering a function dedicated to handling single differences in time series data. This is valuable for various applications, such as identifying trends or preparing data for further analysis. This is a helper function for auto_stationarize().
3. util_doublediff_ts() – Double Differences for Time Series
Building on the concept of differencing, util_doublediff_ts() seems to provide a higher level of sophistication, allowing users to perform double differences on time series data. This could be pivotal in cases where a more refined analysis is required. This is a helper function for auto_stationarize().
4. util_difflog_ts() – Combining Differences and Log Transformation
The fusion of differencing and log transformation in util_difflog_ts() is a remarkable addition. This could be particularly beneficial in scenarios where both operations are needed to unlock deeper insights from the time series data. This is a helper function for auto_stationarize().
5. util_doubledifflog_ts() – Double Differences with Log Transformation
The introduction of util_doubledifflog_ts() appears to take things a step further by combining double differences and log transformation. This function seems poised to provide a comprehensive solution for users dealing with complex time series data. This is a helper function for auto_stationarize().
Minor Fixes and Improvements: Polishing the Experience
1. Attributes Enhancement in ts_growth_rate_vec()
The attention to detail is evident with the addition of attributes to the output of ts_growth_rate_vec(). This enhancement not only improves the clarity of results but also contributes to a more informative and user-friendly experience.
2. Refinement of auto_stationarize() in Response to User Feedback
Updates to auto_stationarize() based on user feedback (Fix #481 #483) demonstrate a commitment to refining existing features. This responsiveness to the community’s needs is commendable and ensures that the package evolves in sync with user expectations. It has taken all of the util_ transforms mentioned above in order to improve it’s functionality.
3. Integration with auto_arima Engine in ts_auto_arima()
The integration of ts_auto_arima() with the parsnip engine of auto_arima is a notable improvement. This update, triggered when .tune is set to FALSE, aligns the package with cutting-edge tools, potentially enhancing the efficiency and accuracy of time series modeling.
In conclusion, the release of healthyR.ts version 0.3.0 is an exciting leap forward. The new features introduce powerful capabilities, while the minor fixes and improvements showcase a commitment to providing a robust and user-friendly package. Users can look forward to a more versatile and refined experience in time series analysis. Great job on this release, and I’m sure the community is eager to explore these enhancements!
Examples
Let’s see how the main functions now behave.
auto_stationarize()
library(healthyR.ts) 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
auto_stationarize(BJsales)
The time series is not stationary. Attempting to make it 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" $ret [1] TRUE
plot.ts(auto_stationarize(BJsales)$stationary_ts)
The time series is not stationary. Attempting to make it stationary...
auto_stationarize(BJsales.lead)
The time series is not stationary. Attempting to make it stationary...
$stationary_ts Time Series: Start = 2 End = 150 Frequency = 1 [1] 0.06 0.25 -0.57 0.58 -0.20 0.23 -0.04 -0.19 0.03 0.42 0.04 0.24 [13] 0.34 -0.46 -0.18 -0.08 0.29 0.56 -0.37 0.20 0.54 -0.31 0.03 0.52 [25] -0.70 0.35 -0.63 0.44 -0.38 -0.01 0.22 0.10 -0.50 0.01 0.30 -0.76 [37] 0.52 0.15 0.06 -0.10 0.21 -0.01 0.70 -0.22 -0.76 0.06 0.02 -0.17 [49] -0.08 0.01 0.11 -0.39 0.01 0.50 -0.02 -0.37 -0.13 0.05 0.54 -0.46 [61] 0.25 -0.52 0.44 0.02 -0.47 0.11 0.06 0.25 -0.35 0.00 -0.06 0.21 [73] -0.09 0.36 0.09 -0.04 -0.20 0.44 -0.23 0.40 -0.01 0.17 0.08 0.58 [85] -0.27 0.79 -0.21 0.02 0.30 0.28 -0.27 -0.01 0.03 0.16 -0.28 0.15 [97] 0.26 -0.36 0.32 -0.11 0.22 -0.65 0.00 0.47 0.16 -0.19 0.48 -0.26 [109] 0.21 0.00 -0.20 0.35 0.38 -0.48 0.20 -0.32 0.43 -0.50 0.12 -0.17 [121] 0.15 -0.36 0.35 -0.03 -0.18 0.16 0.07 0.21 -0.50 0.23 -0.13 0.14 [133] -0.15 0.19 -0.24 0.26 -0.22 0.17 0.37 -0.06 0.29 -0.34 -0.12 -0.16 [145] 0.25 0.08 -0.07 0.26 -0.37 $ndiffs [1] 1 $adf_stats $adf_stats$test_stat [1] -4.838625 $adf_stats$p_value [1] 0.01 $trans_type [1] "diff" $ret [1] TRUE
plot.ts(auto_stationarize(BJsales.lead)$stationary_ts)
The time series is not stationary. Attempting to make it stationary...
ts_auto_arima()
This use to only use the Arima engine if the .tune parameter was set to FALSE, thus it would many times give a simple straight line forecast. This was changed to make the engine auto_arima if .tune is set to FALSE.
library(timetk) library(dplyr) library(modeltime) data <- AirPassengers |> ts_to_tbl() |> select(-index) splits <- time_series_split( data , date_col , assess = 12 , skip = 3 , cumulative = TRUE ) ts_aa <- ts_auto_arima( .data = data, .num_cores = 2, .date_col = date_col, .value_col = value, .rsamp_obj = splits, .formula = value ~ ., .grid_size = 5, .cv_slice_limit = 2, .tune = FALSE ) ts_aa$recipe_info
$recipe_call
recipe(.data = data, .date_col = date_col, .value_col = value,
.formula = value ~ ., .rsamp_obj = splits, .tune = FALSE,
.grid_size = 5, .num_cores = 2, .cv_slice_limit = 2)
$recipe_syntax
[1] "ts_arima_recipe <-"
[2] "\n recipe(.data = data, .date_col = date_col, .value_col = value, .formula = value ~ \n ., .rsamp_obj = splits, .tune = FALSE, .grid_size = 5, .num_cores = 2, \n .cv_slice_limit = 2)"
$rec_obj
ts_aa$model_info
$model_spec
ARIMA Regression Model Specification (regression)
Computational engine: auto_arima
$wflw
══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: arima_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
0 Recipe Steps
── Model ───────────────────────────────────────────────────────────────────────
ARIMA Regression Model Specification (regression)
Computational engine: auto_arima
$fitted_wflw
══ Workflow [trained] ══════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: arima_reg()
── Preprocessor ────────────────────────────────────────────────────────────────
0 Recipe Steps
── Model ───────────────────────────────────────────────────────────────────────
Series: outcome
ARIMA(1,1,0)(0,1,0)[12]
Coefficients:
ar1
-0.2431
s.e. 0.0894
sigma^2 = 109.8: log likelihood = -447.95
AIC=899.9 AICc=900.01 BIC=905.46
$was_tuned
[1] "not_tuned"
ts_aa$model_calibration
$plot
$calibration_tbl
# Modeltime Table
# A tibble: 1 × 5
.model_id .model .model_desc .type .calibration_data
<int> <list> <chr> <chr> <list>
1 1 <workflow> ARIMA(1,1,0)(0,1,0)[12] Test <tibble [12 × 4]>
$model_accuracy
# A tibble: 1 × 9
.model_id .model_desc .type mae mape mase smape rmse rsq
<int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 ARIMA(1,1,0)(0,1,0)[12] Test 18.5 4.18 0.384 4.03 23.9 0.955
ts_aa$model_calibration$plot
Finally enhancement to add attributes to ts_growth_rate_vec()
ts_growth_rate_vec(AirPassengers)
[1] NA 5.3571429 11.8644068 -2.2727273 -6.2015504 11.5702479 [7] 9.6296296 0.0000000 -8.1081081 -12.5000000 -12.6050420 13.4615385 [13] -2.5423729 9.5652174 11.9047619 -4.2553191 -7.4074074 19.2000000 [19] 14.0939597 0.0000000 -7.0588235 -15.8227848 -14.2857143 22.8070175 [25] 3.5714286 3.4482759 18.6666667 -8.4269663 5.5214724 3.4883721 [31] 11.7977528 0.0000000 -7.5376884 -11.9565217 -9.8765432 13.6986301 [37] 3.0120482 5.2631579 7.2222222 -6.2176166 1.1049724 19.1256831 [43] 5.5045872 5.2173913 -13.6363636 -8.6124402 -9.9476440 12.7906977 [49] 1.0309278 0.0000000 20.4081633 -0.4237288 -2.5531915 6.1135371 [55] 8.6419753 3.0303030 -12.8676471 -10.9704641 -14.6919431 11.6666667 [61] 1.4925373 -7.8431373 25.0000000 -3.4042553 3.0837004 12.8205128 [67] 14.3939394 -2.9801325 -11.6040956 -11.5830116 -11.3537118 12.8078818 [73] 5.6768559 -3.7190083 14.5922747 0.7490637 0.3717472 16.6666667 [79] 15.5555556 -4.6703297 -10.0864553 -12.1794872 -13.5036496 17.2995781 [85] 2.1582734 -2.4647887 14.4404332 -1.2618297 1.5974441 17.6100629 [91] 10.4278075 -1.9370460 -12.3456790 -13.8028169 -11.4379085 12.9151292 [97] 2.9411765 -4.4444444 18.2724252 -2.2471910 2.0114943 18.8732394 [103] 10.1895735 0.4301075 -13.4903640 -14.1089109 -12.1037464 10.1639344 [109] 1.1904762 -6.4705882 13.8364780 -3.8674033 4.3103448 19.8347107 [115] 12.8735632 2.8513238 -20.0000000 -11.1386139 -13.6490251 8.7096774 [121] 6.8249258 -5.0000000 18.7134503 -2.4630542 6.0606061 12.3809524 [127] 16.1016949 2.0072993 -17.1735242 -12.0950324 -11.0565111 11.8784530 [133] 2.9629630 -6.2350120 7.1611253 10.0238663 2.3861171 13.3474576 [139] 16.2616822 -2.5723473 -16.1716172 -9.2519685 -15.4013015 10.7692308 attr(,"vector_attributes") attr(,"vector_attributes")$tsp [1] 1949.000 1960.917 12.000 attr(,"vector_attributes")$class [1] "ts" attr(,"name") [1] "AirPassengers"
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.