Time Series Analysis and Decomposition

# File Name: time_series_analysis.R

# Author: Gerard King - www.gerardking.dev

# Title: Time Series Analysis and Decomposition

# Description: This R program generates synthetic time series data, decomposes it into

# trend, seasonal, and residual components, and visualizes the decomposition. It demonstrates

# how to analyze time series data for understanding underlying patterns.

# Use Cases:

# - Analyzing trends in time-dependent data

# - Forecasting future values based on past patterns

# - Detecting seasonality and trends in business, finance, and operations

# Audience:

# - Data analysts working with time series data

# - Economists and business analysts forecasting future trends

# - Students learning about time series analysis and decomposition

# Blue Team Uses:

# - Monitoring and forecasting system performance trends over time

# - Detecting seasonal anomalies in system behavior or network traffic

# - Analyzing time-dependent data to predict potential security threats

# Red Team Uses:

# - Analyzing attack patterns over time to forecast future intrusion attempts

# - Simulating time series data for breach simulations and testing

# - Detecting and modeling seasonal patterns in security breaches or network anomalies

# Current Date: 2025-03-06

# Set the seed for reproducibility

set.seed(789)

# Generate synthetic time series data with trend and seasonality

time <- 1:120  # 120 time points (e.g., 10 years of monthly data)

trend <- time * 0.5  # Linear trend component

seasonality <- sin(2 * pi * time / 12) * 10  # Seasonal component (12 months cycle)

noise <- rnorm(120, mean=0, sd=3)  # Random noise

# Combine components to create the time series

data <- trend + seasonality + noise

# Create a time series object

ts_data <- ts(data, start=c(2015, 1), frequency=12)

# Decompose the time series into trend, seasonal, and residual components

decomposed_ts <- decompose(ts_data)

# Plot the original time series

plot(ts_data, main="Original Time Series", xlab="Time", ylab="Value", col="blue")

# Plot the decomposed components

plot(decomposed_ts)

# Display the decomposition components

cat("Trend component:\n")

print(decomposed_ts$trend)

cat("\nSeasonal component:\n")

print(decomposed_ts$seasonal)

cat("\nResidual component:\n")

print(decomposed_ts$random)

# Print the current date for reference

cat("Date of execution:", Sys.Date(), "\n")