From the course: Python for Time Series Forecasting

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Interpret decomposition models: Additive vs. multiplicative

Interpret decomposition models: Additive vs. multiplicative - Python Tutorial

From the course: Python for Time Series Forecasting

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Interpret decomposition models: Additive vs. multiplicative

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- [Instructor] We create a visual comparison of both the additive model and the multiplicative to interpret the numbers that we see here. First, we'll interpret these charts and then I'll show you how to compute the values so that you understand the full perspective beforehand. In the first chart, we have the real data of the values, which are 2 million megawatts per hour in October 2020, of solar energy generation in California. Over time, we see that there is a growth. At the beginning, 2019, we had 1.24 million megawatts per hour. The minimum value on the following year, we have 1.78. Then on the following, 1.6, we downgrade a bit, and then we keep increasing to 1.84, and finally 2.3. So we see that there is in the minimum value, and of course in the maximum values, an evolving trend of solar energy generation that is reflected in this component by calculating the rolling average each 12 months so that we get the generated energy on average over the rolling months. In other words…

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