In business-science/anomalize: Tidy Anomaly Detection

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = F,
  fig.align = "center"
)

library(dplyr)
library(ggplot2)
library(tidyquant)
library(anomalize)
library(timetk)

Forecasting error can often be reduced 20% to 50% by repairing anomolous data

Example - Reducing Forecasting Error by 32%

We can often get better forecast performance by cleaning anomalous data prior to forecasting. This is the perfect use case for integrating the clean_anomalies() function into your forecast workflow.

library(tidyverse)
library(tidyquant)
library(anomalize)
library(timetk)

# NOTE: timetk now has anomaly detection built in, which 
#  will get the new functionality going forward.
#  Use this script to prevent overwriting legacy anomalize:

anomalize <- anomalize::anomalize
plot_anomalies <- anomalize::plot_anomalies

Here is a short example with the tidyverse_cran_downloads dataset that comes with anomalize. We'll see how we can reduce the forecast error by 32% simply by repairing anomalies.

tidyverse_cran_downloads

Let's take one package with some extreme events. We can hone in on lubridate, which has some outliers that we can fix.

tidyverse_cran_downloads %>%
  ggplot(aes(date, count, color = package)) +
  geom_point(alpha = 0.5) +
  facet_wrap(~ package, ncol = 3, scales = "free_y") +
  scale_color_viridis_d() +
  theme_tq() 

Forecasting Lubridate Downloads

Let's focus on downloads of the lubridate R package.

lubridate_tbl <- tidyverse_cran_downloads %>%
  ungroup() %>%
  filter(package == "lubridate")

First, we'll make a function, forecast_mae(), that can take the input of both cleaned and uncleaned anomalies and calculate forecast error of future uncleaned anomalies.

The modeling function uses the following criteria:

• Split the data into training and testing data that maintains the correct time-series sequence using the prop argument.
• Models the daily time series of the training data set from observed (demonstrates no cleaning) or observed and cleaned (demonstrates improvement from cleaning). Specified by the col_train argument.
• Compares the predictions to the observed values. Specified by the col_test argument.

forecast_mae <- function(data, col_train, col_test, prop = 0.8) {

  predict_expr <- enquo(col_train)
  actual_expr <- enquo(col_test)

  idx_train <- 1:(floor(prop * nrow(data)))

  train_tbl <- data %>% filter(row_number() %in% idx_train)
  test_tbl  <- data %>% filter(!row_number() %in% idx_train)

  # Model using training data (training) 
  model_formula <- as.formula(paste0(quo_name(predict_expr), " ~ index.num + year + quarter + month.lbl + day + wday.lbl"))

  model_glm <- train_tbl %>%
    tk_augment_timeseries_signature() %>%
    glm(model_formula, data = .)

  # Make Prediction
  suppressWarnings({
    # Suppress rank-deficit warning
    prediction <- predict(model_glm, newdata = test_tbl %>% tk_augment_timeseries_signature()) 
    actual     <- test_tbl %>% pull(!! actual_expr)
  })

  # Calculate MAE
  mae <- mean(abs(prediction - actual))

  return(mae)

}

Workflow for Cleaning Anomalies

We will use the anomalize workflow of decomposing (time_decompose()) and identifying anomalies (anomalize()). We use the function, clean_anomalies(), to add new column called "observed_cleaned" that is repaired by replacing all anomalies with the trend + seasonal components from the decompose operation. We can now experiment to see the improvment in forecasting performance by comparing a forecast made with "observed" versus "observed_cleaned"

lubridate_anomalized_tbl <- lubridate_tbl %>%
  time_decompose(count) %>%
  anomalize(remainder) %>%

  # Function to clean & repair anomalous data
  clean_anomalies()

lubridate_anomalized_tbl

Before Cleaning with anomalize

lubridate_anomalized_tbl %>%
  forecast_mae(col_train = observed, col_test = observed, prop = 0.8)

After Cleaning with anomalize

lubridate_anomalized_tbl %>%
  forecast_mae(col_train = observed_cleaned, col_test = observed, prop = 0.8)

32% Reduction in Forecast Error

This is approximately a 32% reduction in forecast error as measure by Mean Absolute Error (MAE).

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Interested in Learning Anomaly Detection?

Business Science offers two 1-hour courses on Anomaly Detection:

• Learning Lab 18 - Time Series Anomaly Detection with anomalize

• Learning Lab 17 - Anomaly Detection with H2O Machine Learning

business-science/anomalize documentation built on Jan. 28, 2024, 1:06 a.m.