From the course: Time Series Analysis and Forecasting with GPT-4o
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Additive decomposition, multiplicative decomposition, and STL decomposition - GPT Tutorial
From the course: Time Series Analysis and Forecasting with GPT-4o
Additive decomposition, multiplicative decomposition, and STL decomposition
Imagine this: just like classical music has its timeless geniuses and modern pop has its current stars, time series analysis features both classical and modern approaches to decomposition. Let's start with the classics: additive and multiplicative decomposition. Additive decomposition is pretty straightforward. It breaks down your data into three parts: the terrain, the seasonal, and the noise or residuals. It's like composing music. The total is just the song of these components. This method is best for stable patterns such as daily temperatures. On the other side, we have multiplicative decomposition Instead of adding up each part, these components multiplied together to make the total value. It's ideal for data that grows or shrinks in proportional ways, for example stock prices, GDP data, and e-commerce sales. But just as music evolves, so does decomposition. Enter the modern trend STL decomposition Seasonal Trend Decomposition using LOESS. This method is flexible. It can handle…
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