Daniel Marthaler and Brian Coffey have an interesting post:
As the year unfolds, our demand fluctuates. Two big drivers of that fluctuation are seasonality and holidays. With the holiday season upon us, it’s a great time to describe how both seasonality and holiday effects can be estimated, and how you can use this formulation in a predictive time series model.
In this post, we describe the difference between seasonality and holiday effects, posit a general Bayesian Holiday Model, and show how that model performs on some Google Trends data.
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