Why Anomalies Matter

In every modern business, data is constantly generated—from transactions and logins to sensor readings and website views. But buried inside this stream of data are rare, unusual, and unexpected events that can reveal fraud, operational failures, or meaningful behavioural shifts. Identifying these events—anomaly detection—has become a core capability for organizations in BFSI, healthcare, ecommerce, industrial operations, and more.
This article walks through the essentials of anomaly detection, showcases two powerful R-based workflows (AnomalyDetection and anomalize), and explains how to interpret anomalies in a business context. By the end, you will know how to build a robust anomaly-detection pipeline and operationalize it for real value.

Why Anomalies Matter
Consider a credit-card issuer. A customer who typically spends around $25 per week suddenly makes a single purchase of $700. The spike is not inherently malicious—perhaps it’s a flight ticket or a one-time household purchase. But from the bank’s standpoint, this sudden deviation is a behavioural anomaly that warrants investigation. It could indicate:
A fraudulent transaction
A card compromise
A real-world lifestyle change
A backend system error
A marketing or pricing trigger
Across industries, anomalies manifest as unusual events relative to an expected pattern. They may occur as spikes, drops, shifts, or structural changes in a time series. Detecting them early reduces financial risk, improves operational resilience, and strengthens customer trust.
This is why AI consulting companies increasingly package anomaly detection into real-time monitoring stacks using analytics platforms (Power BI, Tableau), ML models, and automated alerting systems.

How We Detect Anomalies
At its core, an anomaly is an observation that deviates from what is statistically expected. While anomaly detection methods vary, most follow three principles:
Model the normal behaviour (trend, seasonality, noise).
Identify deviations from this baseline.
Classify and operationalize the anomalies.
This article focuses on time-series methods, but anomalies can also be detected using clustering, dimensionality reduction, or distance-based methods in multivariate datasets. For example, hierarchical clustering can group normal behaviour together while isolating unusual points.
Here, we will cover:
Seasonal Hybrid ESD (SH-ESD) via the AnomalyDetection package
A complete tidyverse workflow via the anomalize package (decompose → detect → visualize)
We illustrate both using:
Wikipedia pageviews for “FIFA”
Bitcoin daily prices

Preparing the Data
Time-series anomaly detection requires:
A date/timestamp column
One or more numeric measures (views, transactions, prices, etc.)
Install and load required libraries
install.packages("devtools")
install.packages("Rcpp")
library(devtools)
install_github("petermeissner/wikipediatrend")
install_github("twitter/AnomalyDetection")

library(Rcpp)
library(wikipediatrend)
library(AnomalyDetection)

Example: Download FIFA pageviews
fifa_data_wikipedia = wp_trend("fifa", from="2013-03-18", lang = "en")

columns_to_keep = c("date","views")
fifa_data_wikipedia = fifa_data_wikipedia[, columns_to_keep]

Example 1: Detecting Anomalies in Wikipedia Pageviews
Before running models, always plot the series to visually inspect patterns. FIFA pageviews typically spike around tournaments, announcements, or major football news.
Apply SH-ESD anomaly detection
anomalies = AnomalyDetectionTs(fifa_data_wikipedia, direction="pos", plot=TRUE)
anomalies$plot

This method highlights points where pageviews rise significantly above expected behaviour.
View anomaly timestamps
anomalies$anoms

The output lists dates and anomalous values. Each anomaly must be correlated with contextual events such as:
Major matches
Player controversies
Media announcements
Seasonal traffic patterns
If no external reason exists, the spike might indicate a data issue or unexpected user behaviour.

Example 2: Anomaly Detection with the anomalize Workflow (Bitcoin Prices)
anomalize provides a more modern, tidyverse-oriented pipeline. It works by:
Decomposing the series into trend, seasonal, remainder
Detecting anomalies in the remainder
Recomposing and visualizing anomalies clearly
Install and load
install.packages('anomalize')
library(devtools)
install_github("business-science/anomalize")

library(anomalize)
library(tidyverse)
library(coindeskr)

Download Bitcoin data
bitcoin_data = get_historic_price(start = "2017-01-01")

bitcoin_data_ts = bitcoin_data %>%
rownames_to_column() %>%
as.tibble() %>%
mutate(date = as.Date(rowname)) %>%
select(-one_of('rowname'))

Decompose and identify anomalies
bitcoin_data_ts %>%
time_decompose(Price, method = "stl") %>%
anomalize(remainder, method = "gesd") %>%
plot_anomaly_decomposition()

This shows how individual anomalies emerge from the remainder after removing seasonality and trend.
Recompose and visualize
bitcoin_data_ts %>%
time_decompose(Price) %>%
anomalize(remainder) %>%
time_recompose() %>%
plot_anomalies(time_recomposed = TRUE)

Extract anomaly rows
anomalies = bitcoin_data_ts %>%
time_decompose(Price) %>%
anomalize(remainder) %>%
time_recompose() %>%
filter(anomaly == 'Yes')

Solution Patterns & Operational Recommendations
Building anomaly detection is only half the job. The real value lies in operationalizing the process.

1. Data Hygiene & Feature Engineering Ensure clean timestamps Remove irrelevant columns Add useful features (weekday, hour, user segment) Handle missing values correctly Well-structured data significantly improves anomaly accuracy.
2. Choose the Right Detection Method SH-ESD (AnomalyDetection) → ideal for seasonal, noisy time series anomalize workflow → best for tidyverse environments and exploratory analysis Multivariate models → required when anomalies depend on multiple correlated features Examples: Isolation Forest, Autoencoders, PCA, Mahalanobis distance.
3. Control Directionality & Sensitivity Specify whether you want to detect: Only spikes Only dips Both Tune alpha and max_anoms to reduce false positives.
4. Add Business Context Enrich anomalies with: Marketing calendars News events Engineering logs System maintenance windows Context separates “noise” anomalies from meaningful incidents.
5. Operationalize with Alerts Integrate anomaly detection with: Email/SMS alerts Slack/Teams notifications Incident management tools (Jira, ServiceNow) Power BI / Tableau dashboards Create clear triage playbooks (check fraud, inspect logs, contact customer, etc.).
6. Build Monitoring & Feedback Loops Over time: Measure precision & recall Let analysts label anomaly outcomes Retrain or adjust detection thresholds This improves accuracy and reduces alert fatigue.

Why Anomaly Detection Matters
Detecting anomalies early helps organizations:
Prevent fraud before loss occurs
Identify system failures before they escalate
Monitor key customer behaviours
Improve forecasting and planning
Increase trust in data-driven systems
With the right workflows and operational playbooks, anomalies become actionable insights—not just statistical outliers.

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