STL (Seasonal-Trend decomposition using LOESS)

Information

• The STL (Seasonal-Trend decomposition using LOESS) is a method used to decompose time series data into three main components: trend, seasonal, and residual.
• The trend component represents the long-term pattern or behavior in the time series.
• The seasonal component represents the repeating pattern that occurs within the time series over a fixed period of time, such as weekly or monthly.
• The residual component represents the remaining fluctuations or noise in the time series after the trend and seasonal components have been extracted.
• The STL algorithm works by first applying a moving average to the time series to remove the seasonal component.
• The trend is then estimated by applying a low-pass filter to the detrended data.
• The seasonal component is then obtained by differencing the original data with the estimated trend and applying a moving average to the resulting series to remove the remaining variation.
• Finally, the residual component is obtained by differencing the original data with the sum of the estimated trend and seasonal components.
• The STL method is useful in identifying and removing the trend and seasonal patterns in time series data, allowing for more accurate analysis of the remaining fluctuations or noise in the data.