The goal of stepdwt is to add a recipes:: preprocessing step that converts a dataframe of numeric columns, where each row is a time-series, into their discrete wavelet transformation coefficents. Is this a good idea? Maybe not! This is purely experimental for me at this point.
stepdwt isn’t on CRAN, although I guess one day it could be.
You can install the development version from GitHub with:
# install.packages("devtools")
# install.packages("wavelets")
devtools::install_github("mattsq/stepdwt")This is a basic example which shows you how to solve a common problem:
library(tidymodels)
#> -- Attaching packages ---------------------------------------------------------------------------------------------------------------------- tidymodels 0.1.1 --
#> v broom 0.7.0 v recipes 0.1.13
#> v dials 0.0.8 v rsample 0.0.7
#> v dplyr 1.0.2 v tibble 3.0.3
#> v ggplot2 3.3.2 v tidyr 1.1.2
#> v infer 0.5.3 v tune 0.1.1
#> v modeldata 0.0.2 v workflows 0.1.3
#> v parsnip 0.1.3 v yardstick 0.0.7
#> v purrr 0.3.4
#> -- Conflicts ------------------------------------------------------------------------------------------------------------------------- tidymodels_conflicts() --
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x recipes::step() masks stats::step()
library(wavelets)
library(stepdwt)
create_data <- function(n, ts_length, arima_terms = list(ar = .2)) {
# create an arbitrary set of ar = .2 time series, for testing.
l <- map(1:n, ~ {
set.seed(.x)
return(tibble(id = .x, idx = 1:ts_length, ts = arima.sim(model = arima_terms, n = ts_length)))
})
# bind them together and pivot them so each row is a series, each column is a position
l_df <- bind_rows(l)
df <- pivot_wider(l_df, id_cols = c(id), names_from = c(idx), values_from = c(ts), names_prefix = "ts_")
return(df)
}
df <- create_data(n = 100, ts_length = 100)
head(df)
#> # A tibble: 6 x 101
#> id ts_1 ts_2 ts_3 ts_4 ts_5 ts_6 ts_7 ts_8 ts_9
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 -0.697 0.348 0.808 0.737 -0.158 1.48 0.686 -0.484 -2.31
#> 2 2 0.0839 0.725 -0.0948 1.97 0.254 0.469 1.08 -0.178 -1.08
#> 3 3 0.0245 0.0903 1.13 -0.992 1.07 -0.531 -1.24 -0.964 0.0599
#> 4 4 1.05 -1.07 -0.428 1.81 2.14 0.994 0.215 0.426 0.0401
#> 5 5 -0.266 -0.525 -0.740 -0.434 0.0513 1.24 -0.554 -1.19 -0.396
#> 6 6 0.448 -1.22 0.495 0.144 -1.02 1.52 -0.874 0.478 -0.273
#> # ... with 91 more variables: ts_10 <dbl>, ts_11 <dbl>, ts_12 <dbl>,
#> # ts_13 <dbl>, ts_14 <dbl>, ts_15 <dbl>, ts_16 <dbl>, ts_17 <dbl>,
#> # ts_18 <dbl>, ts_19 <dbl>, ts_20 <dbl>, ts_21 <dbl>, ts_22 <dbl>,
#> # ts_23 <dbl>, ts_24 <dbl>, ts_25 <dbl>, ts_26 <dbl>, ts_27 <dbl>,
#> # ts_28 <dbl>, ts_29 <dbl>, ts_30 <dbl>, ts_31 <dbl>, ts_32 <dbl>,
#> # ts_33 <dbl>, ts_34 <dbl>, ts_35 <dbl>, ts_36 <dbl>, ts_37 <dbl>,
#> # ts_38 <dbl>, ts_39 <dbl>, ts_40 <dbl>, ts_41 <dbl>, ts_42 <dbl>,
#> # ts_43 <dbl>, ts_44 <dbl>, ts_45 <dbl>, ts_46 <dbl>, ts_47 <dbl>,
#> # ts_48 <dbl>, ts_49 <dbl>, ts_50 <dbl>, ts_51 <dbl>, ts_52 <dbl>,
#> # ts_53 <dbl>, ts_54 <dbl>, ts_55 <dbl>, ts_56 <dbl>, ts_57 <dbl>,
#> # ts_58 <dbl>, ts_59 <dbl>, ts_60 <dbl>, ts_61 <dbl>, ts_62 <dbl>,
#> # ts_63 <dbl>, ts_64 <dbl>, ts_65 <dbl>, ts_66 <dbl>, ts_67 <dbl>,
#> # ts_68 <dbl>, ts_69 <dbl>, ts_70 <dbl>, ts_71 <dbl>, ts_72 <dbl>,
#> # ts_73 <dbl>, ts_74 <dbl>, ts_75 <dbl>, ts_76 <dbl>, ts_77 <dbl>,
#> # ts_78 <dbl>, ts_79 <dbl>, ts_80 <dbl>, ts_81 <dbl>, ts_82 <dbl>,
#> # ts_83 <dbl>, ts_84 <dbl>, ts_85 <dbl>, ts_86 <dbl>, ts_87 <dbl>,
#> # ts_88 <dbl>, ts_89 <dbl>, ts_90 <dbl>, ts_91 <dbl>, ts_92 <dbl>,
#> # ts_93 <dbl>, ts_94 <dbl>, ts_95 <dbl>, ts_96 <dbl>, ts_97 <dbl>,
#> # ts_98 <dbl>, ts_99 <dbl>, ts_100 <dbl>Then we preprocess it using a recipe, and get back DWT coefficients!
recipe_check <- recipe(id ~ ., data = df) %>%
step_dwt(ends_with("1"), ends_with("2"))
training_data <- recipe_check %>%
prep() %>%
juice()
head(training_data)
#> # A tibble: 6 x 99
#> ts_3 ts_4 ts_5 ts_6 ts_7 ts_8 ts_9 ts_10 ts_13 ts_14
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.808 0.737 -0.158 1.48 0.686 -0.484 -2.31 0.663 0.944 1.01
#> 2 -0.0948 1.97 0.254 0.469 1.08 -0.178 -1.08 1.57 0.132 1.04
#> 3 1.13 -0.992 1.07 -0.531 -1.24 -0.964 0.0599 0.164 -0.850 1.05
#> 4 -0.428 1.81 2.14 0.994 0.215 0.426 0.0401 0.0424 0.196 -0.0612
#> 5 -0.740 -0.434 0.0513 1.24 -0.554 -1.19 -0.396 -1.15 -2.32 -0.223
#> 6 0.495 0.144 -1.02 1.52 -0.874 0.478 -0.273 -0.654 -0.756 -0.440
#> # ... with 89 more variables: ts_15 <dbl>, ts_16 <dbl>, ts_17 <dbl>,
#> # ts_18 <dbl>, ts_19 <dbl>, ts_20 <dbl>, ts_23 <dbl>, ts_24 <dbl>,
#> # ts_25 <dbl>, ts_26 <dbl>, ts_27 <dbl>, ts_28 <dbl>, ts_29 <dbl>,
#> # ts_30 <dbl>, ts_33 <dbl>, ts_34 <dbl>, ts_35 <dbl>, ts_36 <dbl>,
#> # ts_37 <dbl>, ts_38 <dbl>, ts_39 <dbl>, ts_40 <dbl>, ts_43 <dbl>,
#> # ts_44 <dbl>, ts_45 <dbl>, ts_46 <dbl>, ts_47 <dbl>, ts_48 <dbl>,
#> # ts_49 <dbl>, ts_50 <dbl>, ts_53 <dbl>, ts_54 <dbl>, ts_55 <dbl>,
#> # ts_56 <dbl>, ts_57 <dbl>, ts_58 <dbl>, ts_59 <dbl>, ts_60 <dbl>,
#> # ts_63 <dbl>, ts_64 <dbl>, ts_65 <dbl>, ts_66 <dbl>, ts_67 <dbl>,
#> # ts_68 <dbl>, ts_69 <dbl>, ts_70 <dbl>, ts_73 <dbl>, ts_74 <dbl>,
#> # ts_75 <dbl>, ts_76 <dbl>, ts_77 <dbl>, ts_78 <dbl>, ts_79 <dbl>,
#> # ts_80 <dbl>, ts_83 <dbl>, ts_84 <dbl>, ts_85 <dbl>, ts_86 <dbl>,
#> # ts_87 <dbl>, ts_88 <dbl>, ts_89 <dbl>, ts_90 <dbl>, ts_93 <dbl>,
#> # ts_94 <dbl>, ts_95 <dbl>, ts_96 <dbl>, ts_97 <dbl>, ts_98 <dbl>,
#> # ts_99 <dbl>, ts_100 <dbl>, id <int>, W11 <dbl>, W12 <dbl>, W13 <dbl>,
#> # W14 <dbl>, W15 <dbl>, W16 <dbl>, W17 <dbl>, W18 <dbl>, W19 <dbl>,
#> # W110 <dbl>, W21 <dbl>, W22 <dbl>, W23 <dbl>, W24 <dbl>, W25 <dbl>,
#> # W31 <dbl>, W32 <dbl>, W4 <dbl>You can then use this to fit a model. Here we do a very silly example, just for the purposes of illustration:
#> i Bootstrap1: recipe
#> v Bootstrap1: recipe
#> i Bootstrap1: model
#> v Bootstrap1: model
#> i Bootstrap1: model (predictions)
#> i Bootstrap2: recipe
#> v Bootstrap2: recipe
#> i Bootstrap2: model
#> v Bootstrap2: model
#> i Bootstrap2: model (predictions)
#> i Bootstrap3: recipe
#> v Bootstrap3: recipe
#> i Bootstrap3: model
#> v Bootstrap3: model
#> i Bootstrap3: model (predictions)
#> i Bootstrap4: recipe
#> v Bootstrap4: recipe
#> i Bootstrap4: model
#> v Bootstrap4: model
#> i Bootstrap4: model (predictions)
#> i Bootstrap5: recipe
#> v Bootstrap5: recipe
#> i Bootstrap5: model
#> v Bootstrap5: model
#> i Bootstrap5: model (predictions)
#> # A tibble: 2 x 5
#> .metric .estimator mean n std_err
#> <chr> <chr> <dbl> <int> <dbl>
#> 1 rmse standard 28.0 5 0.520
#> 2 rsq standard 0.0746 5 0.0133