Why Use STL?
STL has several advantages:
- It can handle any type of seasonality (hourly, daily, weekly, etc.).
- It is robust to outliers.
- It allows the seasonal component to change over time.
- It provides a clear decomposition which can be useful for forecasting and understanding the underlying patterns.
Installing Required Packages
Before we dive into the analysis, ensure that you have R and the necessary packages installed. The primary package used for STL decomposition in R is forecast.
install.packages("forecast")
install.packages("ggplot2") # For plotting
Loading Data
For demonstration, we will use the AirPassengers dataset, which contains monthly totals of international airline passengers from 1949 to 1960.
library(forecast)
data("AirPassengers")
ts_data <- AirPassengers
Performing STL Decomposition
The stl() function in R is used for STL decomposition. Here is how you can apply it to the AirPassengers data:
stl_decomp <- stl(ts_data, s.window = "periodic")
s.window specifies the seasonal smoothing parameter. Using "periodic" indicates that the seasonal component is fixed over time.
Visualizing the Decomposition
To visualize the STL decomposition, we can use the autoplot() function from the ggplot2 package.
library(ggplot2)
autoplot(stl_decomp)
Output:
STL Trend of Time Series Using R
This will provide a plot with the original time series, the seasonal component, the trend component, and the residuals.
Extracting the Components
You can extract the individual components of the decomposition as follows:
seasonal_component <- stl_decomp$time.series[, "seasonal"]
trend_component <- stl_decomp$time.series[, "trend"]
residual_component <- stl_decomp$time.series[, "remainder"]
Analyzing the Trend Component
The trend component is particularly useful for understanding the long-term direction of the time series data. You can plot the trend component separately for a clearer view:
plot(trend_component, main = "Trend Component", ylab = "Passengers", xlab = "Time")
Output:
STL Trend of Time Series Using R
STL Trend of Time Series Using R
Analyzing time series data is crucial in various fields such as finance, economics, meteorology, and many others. One of the powerful techniques for decomposing time series data is the STL (Seasonal and Trend decomposition using Loess) method. This article will provide a comprehensive guide on using STL trend decomposition in the R Programming Language.
Contact Us