What is STL Trend Decomposition?
STL is a robust and versatile method for decomposing a time series into three components:
- Seasonal: The repeating pattern or cycle in the data.
- Trend: The long-term progression in the data.
- Residual: The remaining variation in the data after removing the seasonal and trend components.
The STL method uses Loess (local regression) to estimate the seasonal and trend components, making it flexible and adaptable to a wide range of time series data.
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.
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