Apply 11
Alex Nelson
2024-12-12
# for Core packages
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# for financial analysis
library(tidyquant)## Loading required package: PerformanceAnalytics
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## as.zoo.data.frame zoo# for times series
library(timetk)Goal: Apply Matt Dancho’s tutorial to state unemployment initial claims of New England states.
The following is the replication of Matt Dancho’s tutorial on this page
start_date <- "1989-01-01"
symbols_txt <- c("CTICLAIMS", # Connecticut
"MEICLAIMS", # Maine
"MAICLAIMS", # Massachusetts
"NHICLAIMS", # New Hampshire
"RIICLAIMS", # Rhode Island
"VTICLAIMS") # Vermont
claims_tbl <- tq_get(symbols_txt, get = "economic.data", from = start_date) %>%
mutate(symbol = fct_recode(symbol,
"Connecticut" = "CTICLAIMS",
"Maine" = "MEICLAIMS",
"Massachusetts" = "MAICLAIMS",
"New Hampshire" = "NHICLAIMS",
"Rhode Island" = "RIICLAIMS",
"Vermont" = "VTICLAIMS")) %>%
rename(claims = price)Plotting time series
claims_tbl## # A tibble: 11,244 × 3
## symbol date claims
## <fct> <date> <int>
## 1 Connecticut 1989-01-07 8345
## 2 Connecticut 1989-01-14 6503
## 3 Connecticut 1989-01-21 3821
## 4 Connecticut 1989-01-28 4663
## 5 Connecticut 1989-02-04 4162
## 6 Connecticut 1989-02-11 4337
## 7 Connecticut 1989-02-18 4079
## 8 Connecticut 1989-02-25 3556
## 9 Connecticut 1989-03-04 3826
## 10 Connecticut 1989-03-11 3515
## # ℹ 11,234 more rowsclaims_tbl %>%
plot_time_series(.date_var = date, .value = claims)claims_tbl %>% count(symbol)## # A tibble: 6 × 2
## symbol n
## <fct> <int>
## 1 Connecticut 1874
## 2 Massachusetts 1874
## 3 Maine 1874
## 4 New Hampshire 1874
## 5 Rhode Island 1874
## 6 Vermont 1874claims_tbl %>%
group_by(symbol) %>%
plot_time_series(
.date_var = date,
.value = claims,
.facet_ncol = 2,
.facet_scales = "free",
.interactive = FALSE)
Box plots
claims_tbl %>% count(symbol)## # A tibble: 6 × 2
## symbol n
## <fct> <int>
## 1 Connecticut 1874
## 2 Massachusetts 1874
## 3 Maine 1874
## 4 New Hampshire 1874
## 5 Rhode Island 1874
## 6 Vermont 1874claims_tbl %>%
filter_by_time(.date_var = date, .end_date = "1995") %>%
group_by(symbol) %>%
plot_time_series_boxplot(.date_var = date,
.value = log(claims),
.period = "1 year",
.facet_ncol = 2)Regression plots
claims_tbl %>%
group_by(symbol) %>%
plot_time_series_regression(
.date_var = date,
.facet_ncol = 3,
.formula = log(claims) ~ as.numeric(date) + month(date, label = TRUE),
.show_summary = FALSE)Plotting Seasonality and Correlation
Correlation Plots
claims_tbl %>%
group_by(symbol) %>%
plot_acf_diagnostics(date, claims,
.lags = "1 years") Seasonality
claims_tbl %>%
plot_seasonal_diagnostics(date, log(claims))claims_tbl %>% count(symbol)## # A tibble: 6 × 2
## symbol n
## <fct> <int>
## 1 Connecticut 1874
## 2 Massachusetts 1874
## 3 Maine 1874
## 4 New Hampshire 1874
## 5 Rhode Island 1874
## 6 Vermont 1874claims_tbl %>%
group_by(symbol) %>%
plot_seasonal_diagnostics(date, log(claims))STL Diagnostics
claims_tbl %>%
group_by(symbol) %>%
plot_stl_diagnostics(
date, claims,
.feature_set = c("observed", "season", "trend", "remainder"))## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 yearTime Series Data Wrangling
Summarize by Time
claims_tbl %>%
group_by(symbol) %>%
plot_time_series(date, log(claims), .facet_ncol = 2, .interactive = FALSE)
claims_tbl %>%
group_by(symbol) %>%
summarise_by_time(.date_var = date, claims = mean(log(claims)), .by = "year") %>%
plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)
Filter By Time
claims_tbl %>%
group_by(symbol) %>%
filter_by_time(.date_var = date,
.start_date = "2022-01",
.end_date = "2022") %>%
plot_time_series(date, claims, .facet_ncol = 2)Padding Data
claims_tbl %>%
group_by(symbol) %>%
pad_by_time(date, .by = "week", .pad_value = 0)## # A tibble: 11,244 × 3
## # Groups: symbol [6]
## symbol date claims
## <fct> <date> <int>
## 1 Connecticut 1989-01-07 8345
## 2 Connecticut 1989-01-14 6503
## 3 Connecticut 1989-01-21 3821
## 4 Connecticut 1989-01-28 4663
## 5 Connecticut 1989-02-04 4162
## 6 Connecticut 1989-02-11 4337
## 7 Connecticut 1989-02-18 4079
## 8 Connecticut 1989-02-25 3556
## 9 Connecticut 1989-03-04 3826
## 10 Connecticut 1989-03-11 3515
## # ℹ 11,234 more rowsSliding (Rolling) Calculations
claims_tbl %>%
head(10) %>%
mutate(rolling_avg_2 = slidify_vec(log(claims), mean,
.period = 2,
.align = "right",
.partial = TRUE))## # A tibble: 10 × 4
## symbol date claims rolling_avg_2
## <fct> <date> <int> <dbl>
## 1 Connecticut 1989-01-07 8345 9.03
## 2 Connecticut 1989-01-14 6503 8.90
## 3 Connecticut 1989-01-21 3821 8.51
## 4 Connecticut 1989-01-28 4663 8.35
## 5 Connecticut 1989-02-04 4162 8.39
## 6 Connecticut 1989-02-11 4337 8.35
## 7 Connecticut 1989-02-18 4079 8.34
## 8 Connecticut 1989-02-25 3556 8.24
## 9 Connecticut 1989-03-04 3826 8.21
## 10 Connecticut 1989-03-11 3515 8.21library(h2o)##
## ----------------------------------------------------------------------
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## Your next step is to start H2O:
## > h2o.init()
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## For H2O package documentation, ask for help:
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## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
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## • Use suppressPackageStartupMessages() to eliminate package startup messagesclaims_tbl %>%
# h2o requires all variables to be either numeric or factors
mutate(across(where(is.character), factor))## # A tibble: 11,244 × 3
## symbol date claims
## <fct> <date> <int>
## 1 Connecticut 1989-01-07 8345
## 2 Connecticut 1989-01-14 6503
## 3 Connecticut 1989-01-21 3821
## 4 Connecticut 1989-01-28 4663
## 5 Connecticut 1989-02-04 4162
## 6 Connecticut 1989-02-11 4337
## 7 Connecticut 1989-02-18 4079
## 8 Connecticut 1989-02-25 3556
## 9 Connecticut 1989-03-04 3826
## 10 Connecticut 1989-03-11 3515
## # ℹ 11,234 more rowsSplit Data
set.seed(1234)
data_split <- initial_split(claims_tbl, strata = "claims")
train_tbl <- training(data_split)
test_tbl <- testing(data_split)Recipes
recipe_obj <- recipe(claims ~ ., data = train_tbl) %>%
# Remove zero variance variables
step_zv(all_predictors()) Model
# Initialize h2o
h2o.init()## Connection successful!
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## R is connected to the H2O cluster:
## H2O cluster uptime: 17 hours 4 minutes
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 11 months and 21 days
## H2O cluster name: H2O_started_from_R_alexnelson_hps198
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.46 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
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## R Version: R version 4.2.1 (2022-06-23)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (11 months and 21 days) old. There may be a newer version available.
## Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.htmlsplit.h20 <- h2o.splitFrame(as.h2o(train_tbl), ratios = c(0.85), seed = 5639)##
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valid_h2o <- split.h20[[2]]
test_h2o <- as.h2o(test_tbl)##
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x <- setdiff(names(train_tbl), y)
models_h2o <- h2o.automl(
x = x,
y = y,
training_frame = train_h2o,
validation_frame = valid_h2o,
leaderboard_frame = test_h2o,
max_runtime_secs = 30,
max_models = 10,
exclude_algos = "DeepLearning",
nfolds = 5,
seed = 3456
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## 12:18:28.224: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models.
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|======================================================================| 100%models_h2o %>% typeof()## [1] "S4"models_h2o %>% slotNames()## [1] "project_name" "leader" "leaderboard" "event_log"
## [5] "modeling_steps" "training_info"models_h2o@leaderboard## model_id mean_per_class_error logloss rmse
## 1 XGBoost_1_AutoML_6_20241212_121828 0.5438247 1.161853 0.6476953
## mse
## 1 0.4195092
##
## [1 row x 5 columns]models_h2o@leader## Model Details:
## ==============
##
## H2OMultinomialModel: xgboost
## Model ID: XGBoost_1_AutoML_6_20241212_121828
## Model Summary:
## number_of_trees
## 1 71
##
##
## H2OMultinomialMetrics: xgboost
## ** Reported on training data. **
##
## Training Set Metrics:
## =====================
##
## Extract training frame with `h2o.getFrame("AutoML_6_20241212_121828_training_RTMP_sid_a059_5")`
## MSE: (Extract with `h2o.mse`) 0.2778627
## RMSE: (Extract with `h2o.rmse`) 0.5271268
## Logloss: (Extract with `h2o.logloss`) 0.7652044
## Mean Per-Class Error: 0.2698747
## AUC: (Extract with `h2o.auc`) NaN
## AUCPR: (Extract with `h2o.aucpr`) NaN
## R^2: (Extract with `h2o.r2`) 0.9060138
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,train = TRUE)`)
## =========================================================================
## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## Connecticut Maine Massachusetts New Hampshire Rhode Island
## Connecticut 1018 4 179 0 28
## Maine 61 770 10 103 222
## Massachusetts 59 1 1090 0 10
## New Hampshire 19 127 5 663 85
## Rhode Island 123 206 20 53 764
## Vermont 8 62 0 156 40
## Totals 1288 1170 1304 975 1149
## Vermont Error Rate
## Connecticut 0 0.1717 = 211 / 1,229
## Maine 63 0.3735 = 459 / 1,229
## Massachusetts 0 0.0603 = 70 / 1,160
## New Hampshire 276 0.4357 = 512 / 1,175
## Rhode Island 24 0.3580 = 426 / 1,190
## Vermont 943 0.2200 = 266 / 1,209
## Totals 1306 0.2703 = 1,944 / 7,192
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,train = TRUE)`
## =======================================================================
## Top-6 Hit Ratios:
## k hit_ratio
## 1 1 0.729700
## 2 2 0.924639
## 3 3 0.979978
## 4 4 0.994855
## 5 5 0.999027
## 6 6 1.000000
##
##
##
##
## H2OMultinomialMetrics: xgboost
## ** Reported on validation data. **
##
## Validation Set Metrics:
## =====================
##
## Extract validation frame with `h2o.getFrame("RTMP_sid_a059_7")`
## MSE: (Extract with `h2o.mse`) 0.4182965
## RMSE: (Extract with `h2o.rmse`) 0.6467584
## Logloss: (Extract with `h2o.logloss`) 1.149908
## Mean Per-Class Error: 0.5700614
## AUC: (Extract with `h2o.auc`) NaN
## AUCPR: (Extract with `h2o.aucpr`) NaN
## R^2: (Extract with `h2o.r2`) 0.8491219
## Confusion Matrix: Extract with `h2o.confusionMatrix(<model>,valid = TRUE)`)
## =========================================================================
## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## Connecticut Maine Massachusetts New Hampshire Rhode Island
## Connecticut 126 2 44 0 18
## Maine 20 51 3 37 77
## Massachusetts 48 1 178 0 6
## New Hampshire 4 38 0 40 26
## Rhode Island 27 96 3 19 43
## Vermont 2 12 0 72 12
## Totals 227 200 228 168 182
## Vermont Error Rate
## Connecticut 0 0.3368 = 64 / 190
## Maine 22 0.7571 = 159 / 210
## Massachusetts 0 0.2361 = 55 / 233
## New Hampshire 109 0.8157 = 177 / 217
## Rhode Island 5 0.7772 = 150 / 193
## Vermont 99 0.4975 = 98 / 197
## Totals 235 0.5669 = 703 / 1,240
##
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,valid = TRUE)`
## =======================================================================
## Top-6 Hit Ratios:
## k hit_ratio
## 1 1 0.433065
## 2 2 0.774193
## 3 3 0.906452
## 4 4 0.987903
## 5 5 0.996774
## 6 6 1.000000
##
##
##
##
## H2OMultinomialMetrics: xgboost
## ** Reported on cross-validation data. **
## ** 5-fold cross-validation on training data (Metrics computed for combined holdout predictions) **
##
## Cross-Validation Set Metrics:
## =====================
##
## Extract cross-validation frame with `h2o.getFrame("AutoML_6_20241212_121828_training_RTMP_sid_a059_5")`
## MSE: (Extract with `h2o.mse`) 0.3951766
## RMSE: (Extract with `h2o.rmse`) 0.6286307
## Logloss: (Extract with `h2o.logloss`) 1.090599
## Mean Per-Class Error: 0.498862
## AUC: (Extract with `h2o.auc`) NaN
## AUCPR: (Extract with `h2o.aucpr`) NaN
## R^2: (Extract with `h2o.r2`) 0.8663328
## Hit Ratio Table: Extract with `h2o.hit_ratio_table(<model>,xval = TRUE)`
## =======================================================================
## Top-6 Hit Ratios:
## k hit_ratio
## 1 1 0.500417
## 2 2 0.794077
## 3 3 0.919216
## 4 4 0.990823
## 5 5 0.997219
## 6 6 1.000000
##
##
##
##
## Cross-Validation Metrics Summary:
## mean sd cv_1_valid cv_2_valid cv_3_valid
## accuracy 0.500415 0.012931 0.512856 0.506602 0.498609
## auc NA 0.000000 NA NA NA
## err 0.499585 0.012931 0.487144 0.493398 0.501391
## err_count 718.600000 18.420097 701.000000 710.000000 721.000000
## logloss 1.090601 0.011888 1.083716 1.084928 1.083350
## max_per_class_error 0.748112 0.021781 0.760684 0.727660 0.744681
## mean_per_class_accuracy 0.501129 0.013093 0.513660 0.507247 0.499673
## mean_per_class_error 0.498871 0.013093 0.486340 0.492753 0.500327
## mse 0.395178 0.005633 0.391113 0.391439 0.394302
## pr_auc NA 0.000000 NA NA NA
## r2 0.866332 0.001933 0.867991 0.867637 0.866435
## rmse 0.628619 0.004463 0.625390 0.625651 0.627935
## cv_4_valid cv_5_valid
## accuracy 0.504868 0.479138
## auc NA NA
## err 0.495132 0.520862
## err_count 712.000000 749.000000
## logloss 1.089619 1.111390
## max_per_class_error 0.728814 0.778723
## mean_per_class_accuracy 0.505597 0.479469
## mean_per_class_error 0.494403 0.520531
## mse 0.394132 0.404902
## pr_auc NA NA
## r2 0.866496 0.863100
## rmse 0.627799 0.636319